{"as_of":"2026-08-16T18:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:26424e89dbcaacefa28bb8322396e4fbb5420d03f155f9608465f130f912950e","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:45:14.244387Z","state":"measured"},{"denominator":66,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":66,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:00:44.892647Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T20:00:45.405278Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"cited_work":{"arxiv_id":"2505.12106","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.12106","snapshot_observed_at":"2026-08-06T20:00:45.405278Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","venue":"cs.CR","work_id":"9d954f4a-0352-4cf3-ba9b-16089087e4e7","year":2025},"citing_paper":{"arxiv_id":"2507.03993","last_updated":"2025-07-05T10:45:45Z","snapshot_observed_at":"2026-08-10T02:48:27.960616Z","submitted_at":"2025-07-05T10:45:45Z","title":"MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:00:44.892647Z"},"links":{"cited_paper":"/paper/2505.12106","citing_paper":"/paper/2507.03993"},"observation_digest":"sha256:e16c4527244c489ce3879a16527083c4234ce5f4fd19030a09430599889982d8","observation_id":"e1223d83-e9f5-451f-993e-08a5b40d0d22","resolution":{"observed_at":"2026-08-06T20:00:45.409503Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.12106/citation-record","integrity":"/paper/2505.12106/integrity","json":"/paper/2505.12106/citation-record.json","paper":"/paper/2505.12106"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.213486Z","title":"Market share of mobile operating systems worldwide from 2009 to 2024, by quarter","venue":null,"work_id":"8a9f2782-3c1c-47ce-93ac-c2c60b86d8c5","year":2009},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.963631Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:4323be0b2ea160f6921c3d32954882c0a61a9d5e2f36a90db79cd563b81b0165","observation_id":"5bd4c626-eb27-4085-b714-21f1b7e74983","resolution":{"observed_at":"2026-08-15T20:45:15.217787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.199104Z","title":"Smartphone operating system share by age group in the u.s","venue":null,"work_id":"4a1e5bb8-d050-44e8-976a-df2b4efcca1b","year":2023},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.969005Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:fb68f6b7c3a3ddad5339dcfa32282d41830a06ec83c1a78da4e6cb1880ecc575","observation_id":"f15083cf-acea-42a1-8d8e-502bc273dd89","resolution":{"observed_at":"2026-08-15T20:45:15.204042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.184850Z","title":"Mobile security index (msi) report 2023: Security threats and attacks.https://www.verizon","venue":null,"work_id":"8fb7e61e-4478-4b78-b66a-e793723db194","year":2023},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.973392Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:67303f7ee29512aac6265fdfa6fe1035b6e1c1c03fcf8ba865667660732e992f","observation_id":"ccb8a4e8-8dea-4879-bfbc-637abfad36a0","resolution":{"observed_at":"2026-08-15T20:45:15.189577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00602","last_updated":"2021-02-28T19:55:19Z","snapshot_observed_at":"2026-08-14T01:24:04.662647Z","submitted_at":"2021-02-28T19:55:19Z","title":"Virus-MNIST: A Benchmark Malware Dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00602","snapshot_observed_at":"2026-08-15T20:45:13.977873Z","title":"Virus-mnist: A benchmark malware dataset","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.977873Z"},"links":{"cited_paper":"/paper/2103.00602","citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:35920c674792085321be14972b99e06c7ee7d2fd796aa153e1e1a9e6108a2348","observation_id":"b85052ff-4464-43ea-8091-aa09a80ba62a","resolution":{"observed_at":"2026-08-15T20:45:13.977873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.170887Z","title":"Understanding the spreading patterns of mobile phone viruses","venue":null,"work_id":"9d679e7e-ca8f-4753-91d8-a858c09908fe","year":2009},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.982999Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:9c7344e585d0d1e24240ca8a888b7fe5d1fcf99e6514c960a3bbcc1737117e04","observation_id":"ded69ae6-16ed-4f60-a665-6b97b5f8a03c","resolution":{"observed_at":"2026-08-15T20:45:15.175480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.157556Z","title":"Recent worms: a survey and trends","venue":null,"work_id":"929a5f92-161e-44a6-b4da-47cf19489455","year":2003},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.988184Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:fb8ca28802d6e402f038282871668b71bfdb9ccca91480ab7df09f4f2688acec","observation_id":"4ee63c6b-bf6b-4354-9814-ab8c41b1f0e8","resolution":{"observed_at":"2026-08-15T20:45:15.162127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.143790Z","title":"Adware: a review","venue":null,"work_id":"b8b8354d-2586-46b2-b15a-ff028a13e5b3","year":2015},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.993812Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:c8308cd44f92fdb3f0ad775447249a715b8aa6d90e42081f49d074a9333cce3e","observation_id":"1695cbaa-2b90-4347-80cb-cfc9f57ee2f8","resolution":{"observed_at":"2026-08-15T20:45:15.148555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.129838Z","title":"An analysis of android adware","venue":null,"work_id":"936c66f7-7df5-4751-9eb9-f1c7bac41a48","year":2019},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:13.997946Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:f88bc513a9dba00ff31d174978783edb540a141bda70417c35e50d151bfaa774","observation_id":"b4946de0-5ee8-4e01-920f-bc8d1aeabe95","resolution":{"observed_at":"2026-08-15T20:45:15.134411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.116092Z","title":"Exploring spyware effects","venue":null,"work_id":"6a2d1a90-36f4-4c15-999e-9f41b279b5f9","year":2004},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.002226Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:6966519e76d9b9a011e0cab684d299495123fad3b78fc5861523475d513caa8e","observation_id":"12c780b0-05f2-4181-9bc7-b68cde159f11","resolution":{"observed_at":"2026-08-15T20:45:15.120260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.102637Z","title":"Ransomware: A research and a personal case study of dealing with this nasty malware","venue":null,"work_id":"e9e1cc49-2c40-4cd2-9766-c1ca63fec635","year":2017},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.006595Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:92fadf1e9eca915d8ed2049e7d3a406d4bdfd4458153dd96514a67f095ae698d","observation_id":"059e58b7-3885-44e5-ba00-b2c725bb580f","resolution":{"observed_at":"2026-08-15T20:45:15.107101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.088412Z","title":"Rootkits and their effects on information security.Information Systems Security, 16(3):164–176, 2007","venue":null,"work_id":"beb62d4e-7981-4f80-b276-fb8e699f7fb7","year":2007},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.011166Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:473ba985f73d5a956ddfe482ca9f20b13e16f400b1b2d8469c209bd83db508ba","observation_id":"8e71b712-8b12-4f5a-aa1e-6dffc9ebae4e","resolution":{"observed_at":"2026-08-15T20:45:15.093036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.073547Z","title":"Study on computer trojan horse virus and its prevention","venue":null,"work_id":"8a24d2e1-ad15-4e5b-9547-903b3aff3f5c","year":2015},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.015358Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:2a94bf354dcc85a6c1b17bf89b98e3b8a585a2c24a3b7d3ef65d7e1837dce764","observation_id":"6660f7df-0fab-44b5-993e-9c7a92be8554","resolution":{"observed_at":"2026-08-15T20:45:15.078348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.059683Z","title":"Keyloggers: silent cyber security weapons.Network Security, 2020(2):14– 19, 2020","venue":null,"work_id":"60821bfc-3c8c-48c1-8e74-3fa553832270","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.019608Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:b87d473cb160b5d506c10cbc8059a85526e5ad2d6ec73e388068a57d20b5e58c","observation_id":"802a279f-20ec-4fea-a0a1-f24245c86574","resolution":{"observed_at":"2026-08-15T20:45:15.064464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.045251Z","title":"A survey of botnet and botnet detection","venue":null,"work_id":"c643dd1b-97c6-41a4-b65a-1a5292db0cc8","year":2009},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.024104Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:1c667b6e20cbb22fdc66c75f2b1bf2c27578a0095452bbaa89887ffc1938e779","observation_id":"d238e323-feab-4dbc-876c-f9aa7aa0226d","resolution":{"observed_at":"2026-08-15T20:45:15.050130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.031635Z","title":"A comprehensive survey on identification of malware types and malware classification using machine learning techniques","venue":null,"work_id":"1ce1f300-1cdf-46a3-a429-0bb8f7b198f3","year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.028428Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:df1956e14294aff01390f13d5490643efee3c36dc49e294b1c639a0b36286b3f","observation_id":"5cbf5ccd-3f1e-43fc-bd58-effa361f1d59","resolution":{"observed_at":"2026-08-15T20:45:15.036347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.017742Z","title":"Strengthening digital signatures via randomized hashing","venue":null,"work_id":"d215f492-faef-47c9-8fb6-eb0be93509aa","year":2006},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.032794Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:011e74a5d763709919a4a6eeb04cdb5a93bb2438ccd015c8fb71af7dae7a7ce4","observation_id":"97b14579-7d34-4407-b5bc-08cf4b238f59","resolution":{"observed_at":"2026-08-15T20:45:15.022360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:15.003070Z","title":"Obfuscation techniques against signature-based detection: a case study","venue":null,"work_id":"c45237b7-cb13-43e4-b74e-9b0ba783ca0e","year":2015},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.037331Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:cbadc786c31e322a3271cb560ec9211476db3c2de3530a4a7be56961ebb38249","observation_id":"0a641f1c-9e23-4176-a060-e133f2c305f2","resolution":{"observed_at":"2026-08-15T20:45:15.007717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.988109Z","title":"Datdroid: Dynamic analysis technique in android malware detection","venue":null,"work_id":"65229451-98d2-428d-a84b-30d031d07b69","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.041556Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:e1d0db79193ab6fd6a07fe33a4eacd287d354a5a5c1b981ffe53e2b0e8b250c5","observation_id":"83d44367-fa78-4e5f-b1b8-a07064294b7e","resolution":{"observed_at":"2026-08-15T20:45:14.992852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.972979Z","title":"A systematic literature review of android malware detection using static analysis","venue":null,"work_id":"46be5662-115e-4123-b97e-cd0dac3f774f","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.045720Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:261fcd82b6bbd172cb416a4f577d66a9cfd4c9ba0b4272055e48839caa289fac","observation_id":"43b8a705-6c9e-41e2-b13d-7f461a678809","resolution":{"observed_at":"2026-08-15T20:45:14.978090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.958182Z","title":"Behavior analysis of malware using machine learning","venue":null,"work_id":"7ddbdaf6-c027-4329-be0e-cd3989f44c1c","year":2015},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.049994Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:d1737db5b53a7f5ea7f4ee973b91a01b8013c8e041c4b45a07f578d64c36084b","observation_id":"79ffaa16-32a9-4d59-9616-cf9787cc5c7f","resolution":{"observed_at":"2026-08-15T20:45:14.962734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.944448Z","title":"Sequential digital signatures for cryptographic software-update authenti- cation","venue":null,"work_id":"d21d22c4-ed92-4b32-89a4-73c9940fca01","year":2022},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.053949Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:bc702b405c68b0687da6fb010555bc0dde7a89e3456a8d916be2a34c1cf81574","observation_id":"2d513691-9126-4aa4-899f-78996f22146a","resolution":{"observed_at":"2026-08-15T20:45:14.949168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.930303Z","title":"A study on malware and malware detection techniques","venue":null,"work_id":"e8783972-665d-47d2-9eef-708762c22fbe","year":2018},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.058286Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:88dd2a553c1d61b567086f16c7a62de5b41912893d6b63885094129ece4a9cab","observation_id":"c65901e6-e4c1-4e57-97b2-4425d98ee2aa","resolution":{"observed_at":"2026-08-15T20:45:14.935112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.915962Z","title":"Obfuscation- resilient android malware analysis based on complementary features","venue":null,"work_id":"cb4caf71-689d-4be2-9f58-f2bebb10d132","year":2023},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.062551Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:a1972a2c320de6852d734af2e1557c9cc160401bb21b979265aec75b223e225c","observation_id":"4fdaa355-0b7b-41f0-8515-2d4371b79a15","resolution":{"observed_at":"2026-08-15T20:45:14.920584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.901406Z","title":"The rise of obfuscated android malware and impacts on detection methods","venue":null,"work_id":"922e9093-635d-4853-be74-5d1f3a3cccad","year":2022},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.066398Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:dba9b119bbdd2df602213a1f0dc579b9a44cffe1c3d4aa982250f99993531169","observation_id":"a57f1da0-7c30-4f85-8dcf-554924386bbc","resolution":{"observed_at":"2026-08-15T20:45:14.906144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.886646Z","title":"Malgene: Automatic extraction of malware analysis evasion signature","venue":null,"work_id":"ffbc28a0-7665-42ca-9be2-1474d277f7f2","year":2015},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.070322Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:8e8e4115a516881ffca2849cd5f63d3417e270af4fd35f1766d103e8527e093d","observation_id":"419bb6ff-9650-4494-8595-ec0383c07128","resolution":{"observed_at":"2026-08-15T20:45:14.891558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.870984Z","title":"An approach to dynamic malware analysis based on system and application code split","venue":null,"work_id":"04751ea8-1f95-4fdf-9cde-251255e4b9f6","year":2022},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.074377Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:4c03ea04db53563f3f91f1780eb2edb830a6e9dff994887d526874dc1fe33f95","observation_id":"5c85d958-6d0b-4a23-8f1f-479a7a890b67","resolution":{"observed_at":"2026-08-15T20:45:14.876094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.856771Z","title":"Nmal-droid: network- based android malware detection system using transfer learning and cnn-bigru ensemble","venue":null,"work_id":"12604082-fe5e-43a3-9717-df470b62c705","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.078235Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:f764cbc3732818909a1f91e35251cbcf63bd0c694a09dc3acb6d12cb416faf5f","observation_id":"5d8544e9-bfa2-4962-9c80-f0b26332eb35","resolution":{"observed_at":"2026-08-15T20:45:14.861368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.841096Z","title":"Malware detection approach based on artifacts in memory image and dynamic analysis","venue":null,"work_id":"d47b41fc-f90b-4c3c-a614-69dea4f89949","year":2019},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.082315Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:85543f4a835adebc8bdc33acf5c0d0036100e8fb0020ae47df64029282172841","observation_id":"a4310238-5a56-408c-8bca-006eafbd1f7f","resolution":{"observed_at":"2026-08-15T20:45:14.846464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.825363Z","title":"A new approach to android malware detection using fuzzy logic-based simulated annealing and feature selection","venue":null,"work_id":"d79e95e3-a04b-4776-b8ac-e593f07b5363","year":2023},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.086647Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:6ba1708614670046aa5d98354d515ddd94f9ca306cb29dbcd67a4bf7542c8f73","observation_id":"b23dda90-6937-4992-bea9-b6af96255b13","resolution":{"observed_at":"2026-08-15T20:45:14.830268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.809853Z","title":"Potential of the dynamic approach to data analysis","venue":null,"work_id":"8f0784ee-56e8-42a9-8f8b-b196d7c46a3b","year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.091189Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:6db55e250593dbefbcea96afedfe1cd2cc4ccf026f5f1dc3a0de34030a6f23df","observation_id":"3bb19c9d-895c-4124-b9e5-e90312cbb1b0","resolution":{"observed_at":"2026-08-15T20:45:14.814609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.794596Z","title":"Malware detection in android based on dynamic analysis","venue":null,"work_id":"8da667ca-1f75-42a8-a556-90fea50f6c6f","year":2017},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.095431Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:6e6eb86a1d912879464592c174b38b099128f2f98c0e8d9cb539c8c2228f5edc","observation_id":"05019582-436f-427d-96fe-87cb8440c22a","resolution":{"observed_at":"2026-08-15T20:45:14.799518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.780074Z","title":"Integrated static analysis for malware variants detection","venue":null,"work_id":"0579a14c-0956-4016-a151-6b22bd6afdb7","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.099792Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:2631543a77fca269a47f7219911f326fada4f7bd1d8892c7ef13f20f88a50772","observation_id":"6b5b3a63-666c-4186-87c9-489db62b813f","resolution":{"observed_at":"2026-08-15T20:45:14.784886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.07682","last_updated":"2021-11-07T01:48:30Z","snapshot_observed_at":"2026-08-13T21:02:44.695359Z","submitted_at":"2020-11-16T01:50:21Z","title":"A Large-Scale Database for Graph Representation Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.07682","snapshot_observed_at":"2026-08-15T20:45:14.103994Z","title":"A large-scale database for graph representation learning","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.103994Z"},"links":{"cited_paper":"/paper/2011.07682","citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:27aaaa1f87c36adc48d5c80b84eb555a66d1a722dbbbe395f615e3f39443f779","observation_id":"5f848853-0283-465d-a43a-cf55dba0fd7d","resolution":{"observed_at":"2026-08-15T20:45:14.103994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.764409Z","title":"Hit4mal: Hy- brid image transformation for malware classification.Transactions on Emerging Telecommunications Technologies, 31(11):e3789, 2020","venue":null,"work_id":"c6cb0785-569d-4ede-8d2e-f7025e435233","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.108512Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:cb6417572b51fc94488579208824e143099c2d7298ed2f5afdb2c75f6bb3621f","observation_id":"70e64b02-2e92-4482-ab9c-b10628432c3c","resolution":{"observed_at":"2026-08-15T20:45:14.769678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.748563Z","title":"Dynamic security analysis on android: A systematic literature review","venue":null,"work_id":"5ba9a738-1796-4877-98db-905988e65dee","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.113002Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:6ca44b043fac54c3ba69d9f053b993ce4f17361fc54ae7f1ef3197fe33e38545","observation_id":"fb1ed3b9-6b6b-439c-a050-d0d9da4385f7","resolution":{"observed_at":"2026-08-15T20:45:14.753363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.733218Z","title":"Image visualization based malware detection","venue":null,"work_id":"c61d3493-4aff-40e4-b582-41101f2656c3","year":2013},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.117016Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:4bb5c846929fec8bff3b5ad7a39bfcea4511dae64aaca4df16c05833977962fc","observation_id":"7607d515-19a9-45c3-91fa-d40c601fff85","resolution":{"observed_at":"2026-08-15T20:45:14.737950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.717941Z","title":"Improving android malware detection with entropy bytecode-to-image encoding framework","venue":null,"work_id":"49571cda-a08c-4e70-990a-a973493dd847","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.121149Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:629744c9d152ebe9752a053e63af9af396ab81fc327f59d093656e1925f76b84","observation_id":"e6ca8f5f-696b-41ef-884c-04ce63f2394c","resolution":{"observed_at":"2026-08-15T20:45:14.722783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.702219Z","title":"Euphony: harmonious unification of cacophonous anti-virus vendor labels for android malware","venue":null,"work_id":"df2eaabb-da07-44bc-854e-584f2c63ae3f","year":2017},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.125434Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:4be445f26cc4d23f50a452d345b05ddcbc6d525464accf953c0489711713e684","observation_id":"f5b6a386-4bf5-4c8a-8ac5-e69bb08c7bb2","resolution":{"observed_at":"2026-08-15T20:45:14.707576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.685884Z","title":"https://www.virustotal.com","venue":null,"work_id":"9f60bffb-eb56-410c-9e65-7cc99101eaab","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.129773Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:c1d7bf3af11750858ce10df9536436b7229ea2e59e0ce4c2901044702f92b830","observation_id":"df5fc45a-2006-4ced-82b7-5f3d232c99c3","resolution":{"observed_at":"2026-08-15T20:45:14.691202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.133832Z","title":"Convolutional neural network: a review of models, methodologies and applications to object detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.133832Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:a811d72f1a0d6d60cdfcb7a8ad08d5129ad38416add0179e153206205939f412","observation_id":"d9effebf-163a-462a-bc55-9adcb251be73","resolution":{"observed_at":"2026-08-15T20:45:14.133832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.660436Z","title":"A survey on deep learning-based lane detection algorithms for camera and lidar","venue":null,"work_id":"9d55f2ec-0e89-4167-a32e-3a4362ec966f","year":2025},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.138315Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:e40a96f250d5121dbfaed4b606add52dcfa36aa59d5b0cc4614ce6d3c093fbaa","observation_id":"1d64282a-3b4b-49c4-91de-cf470609565b","resolution":{"observed_at":"2026-08-15T20:45:14.665141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.644988Z","title":"D-ddpm: Deep denoising diffusion probabilistic models for lesion segmentation and data generation in ultrasound imaging","venue":null,"work_id":"95708f9f-ba28-4f7f-8f5a-69ad3c761976","year":2025},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.142540Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:29b9ce750bef3ef9def2a934ac66bbe4b19a1e6c8ad182e9ca5362fed2910515","observation_id":"d1ad85b2-2374-4993-a567-3b2a6212b58f","resolution":{"observed_at":"2026-08-15T20:45:14.649703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.629991Z","title":"Anomaly detection for in-vehicle network using cnn-lstm with attention mechanism","venue":null,"work_id":"4fb0e727-9036-48cd-b2af-d85b2d63cf9d","year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.146844Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:c03ffb9dd7e5c814e53e842f59cc93eb973924012e6c4969033a24d14cc27c10","observation_id":"96e50b04-abc8-49d3-9b18-71274ee9e170","resolution":{"observed_at":"2026-08-15T20:45:14.635058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.614869Z","title":"Androzoo: Collecting millions of android apps for the research community","venue":null,"work_id":"fdca0f55-66af-455d-9814-b80c92f67132","year":2016},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.150886Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:8c999494cc70cadeb50d4ed11c8a4a07e2b4227bd89643f608cc02d19186caaa","observation_id":"963e4144-5ca8-4057-9193-9a245f474bb1","resolution":{"observed_at":"2026-08-15T20:45:14.620002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.155105Z","title":"Drebin: Effective and explainable detection of android malware in your pocket","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.155105Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:11bcc910fb6152e8d890c6640ef4ea5f11e9ab39beaf3bfbf81d4cb079f5e481","observation_id":"01b3e883-a6c2-4c76-bd57-b14735ea1998","resolution":{"observed_at":"2026-08-15T20:45:14.155105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.590463Z","title":"Malnet: A large-scale image database of malicious software","venue":null,"work_id":"d5b6ffe0-0996-4e1d-bb37-37489ce1f0d4","year":2022},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.159596Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:b39c280400579b9c01ffac29b1b0a647d85b713c9ae4c898e7813a61077b2c36","observation_id":"da24e4bf-ecd5-4a59-9da2-04708415b44b","resolution":{"observed_at":"2026-08-15T20:45:14.595201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.576601Z","title":"A pe header-based method for malware detection using clustering and deep embedding techniques","venue":null,"work_id":"93b835c8-0bc4-4af2-b0ac-a2741fc2d1d1","year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.164078Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:326975976cef2a11460ba8412ec34e8a76c8e451000a8591dac049b2202bd79e","observation_id":"e9fe1242-dceb-4773-9fae-54bda259b587","resolution":{"observed_at":"2026-08-15T20:45:14.581187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.563090Z","title":"Malware images: visualization and automatic classification","venue":null,"work_id":"f0b686f1-0561-4513-9aa5-c6070229bd3c","year":2011},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.168571Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:e183c796b569917449849ce8f9855815f17e0bb583067b90435a47cbbcef1ab0","observation_id":"72c5ad82-4de7-4db0-bedb-ab6fc69c66ed","resolution":{"observed_at":"2026-08-15T20:45:14.567759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17190","last_updated":"2025-04-29T22:09:13Z","snapshot_observed_at":"2026-08-16T13:49:02.277228Z","submitted_at":"2024-05-27T14:14:07Z","title":"SoK: Leveraging Transformers for Malware Analysis","version":2},"cited_work":{"arxiv_id":"2405.17190","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.17190","snapshot_observed_at":"2026-08-15T20:45:14.281827Z","title":"SoK: Leveraging Transformers for Malware Analysis","venue":"cs.CR","work_id":"ae544f82-ed2a-4a6b-be9b-7a401cdb7bb4","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.173064Z"},"links":{"cited_paper":"/paper/2405.17190","citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:37bacd3f6909ea5e7747175c44e305f56d308a5ba2945d4d52485e63888107d1","observation_id":"5d578b4d-0538-4c46-b8b4-3813b293a7aa","resolution":{"observed_at":"2026-08-15T20:45:14.288926Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.549490Z","title":"An- drodex: Android dex images of obfuscated malware","venue":null,"work_id":"7e7f8aee-fa68-4283-9d3c-a378a15a2892","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.178008Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:a90e5ee02548c313fe49dcd70cb6c85b3815edc597172fde56d15d5b677022ea","observation_id":"87d2a8d0-7bdc-4fe9-ae26-1839567008f1","resolution":{"observed_at":"2026-08-15T20:45:14.553959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.535271Z","title":"Malware classification with deep convolutional neural networks","venue":null,"work_id":"5ac84202-b2be-4d73-97c1-aa0934f205fd","year":2018},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.182892Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:88ed1154b049529a10f085c4289e9762c81eed1e23307945546526125a937e15","observation_id":"06d23b9e-f295-4937-81a1-ecb3d1148422","resolution":{"observed_at":"2026-08-15T20:45:14.539821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.520911Z","title":"Microsoft malware classification challenge (big 2015)","venue":null,"work_id":"957a348d-f6dc-4907-ae62-a176b98fda2b","year":2015},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.187387Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:36932b2567b2a03d9c288050b6c7cb5de2f536adcd00227a583c603b4f797f9a","observation_id":"467f8867-3392-4d71-9f3c-017a9d36ba4a","resolution":{"observed_at":"2026-08-15T20:45:14.525751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.506082Z","title":"Advandmal: Adversarial training for android malware detection and family classification","venue":null,"work_id":"34c74e58-5b6c-4d30-924d-1316509c99e0","year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.192034Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:0a84df362cabe3a6692d6b2f5f5436f9246bd373d80ab5f3535bcc37db263ca6","observation_id":"82181bdc-1da4-4ef0-9d81-ec4408421990","resolution":{"observed_at":"2026-08-15T20:45:14.511142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.491440Z","title":"Android malware detection based on image-based features and machine learning techniques","venue":null,"work_id":"f570ce11-ac17-4bd2-9a56-4051d9fc7ccb","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.196779Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:605dac155137945647741a26393281e916e4790370fbc08974a606be5da7c6b6","observation_id":"a00d960f-d1f4-4d14-a3bb-55b94af035b6","resolution":{"observed_at":"2026-08-15T20:45:14.496183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.476986Z","title":"Dexray: a simple, yet effective deep learning approach to android malware detection based on image representation of bytecode","venue":null,"work_id":"2941616b-1e61-4e6d-9afb-fd7ba77a6141","year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.200852Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:7e38c58a609b108cf2fd347b79dc27494ba4f4c47ea25bc240361ba26b3aeb3f","observation_id":"8b4fdf0c-62dd-4e88-a1a9-d44b3301b2b2","resolution":{"observed_at":"2026-08-15T20:45:14.482058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.462340Z","title":"A novel malware detection and family classifi- cation scheme for iot based on deam and densenet","venue":null,"work_id":"c4f32e74-e2c9-42b9-b318-6d99d7dfddbc","year":2021},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.205216Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:ae5fc13abb8e6b0aa227e7070a814f0d38e9b3e30b177cda97f8dfd7ed616f09","observation_id":"49ef1ff5-ac47-4ab3-a0ec-37a8ed8f92b9","resolution":{"observed_at":"2026-08-15T20:45:14.467014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.447675Z","title":"Rgb-based android malware detection and classification using convolutional neural network","venue":null,"work_id":"f2ae4c42-1132-409a-b379-f1a525e4d043","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.209435Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:6c2d30b6146ad162c70a767ba8a3f3e4c7199a596ede0decb05c1932b8c478b5","observation_id":"9f77fc8e-f8f6-40ca-bf87-eb87e8ef990b","resolution":{"observed_at":"2026-08-15T20:45:14.452399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.432897Z","title":"Malssl–self-supervised learning for accurate and label-efficient malware classification","venue":null,"work_id":"8482fe4e-1f45-4969-897b-0a1729ca640b","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.213586Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:0257d762da697a382d1af66b0f4cda0542b324c9129132547042d46f635f9822","observation_id":"87053aac-24cf-4541-9452-b47810b6d9b5","resolution":{"observed_at":"2026-08-15T20:45:14.438019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.417677Z","title":"Androguard tool by google","venue":null,"work_id":"57c6c9d3-1f34-47dc-89ef-f00b6e85d7fa","year":2013},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.217597Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:989688334e719314cece7c72e5cab8219a4a9c74095a7858367974f5883aabf9","observation_id":"57314408-d77a-4852-be29-c11922bbe9f2","resolution":{"observed_at":"2026-08-15T20:45:14.422331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.401181Z","title":"(binvis) a library for drawing space-filling curves like the hilbert curve","venue":null,"work_id":"8bc277c1-41c1-4397-a6d3-e6612870dc87","year":2015},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.221692Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:05548fdde9d80d5979077616f2d0a923b93b476eeb51dabac552521018ebdde8","observation_id":"2bb10e33-f93c-4a60-8c51-d901eef9d186","resolution":{"observed_at":"2026-08-15T20:45:14.406531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.384767Z","title":"Malgra: Machine learning and n-gram malware feature extraction and detection system","venue":null,"work_id":"9e40c359-6175-4e2d-935b-031b755ff48f","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.226083Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:cbec6ee3cf3f3fd9ffa65e292aab4d9fe9a0ddf54b21b9f8aa39f01061dd7313","observation_id":"ff5c48de-72a2-40a3-add5-30469c0e6a4f","resolution":{"observed_at":"2026-08-15T20:45:14.389775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.370733Z","title":"Enhancing malware classifica- tion via self-similarity techniques","venue":null,"work_id":"5d52fcde-0520-44dc-b3dd-2f869f959e09","year":2024},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.231177Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:c1b0496e80200df13da71676c71091f31ed6c3482c0999c81fd6150731e3cd41","observation_id":"f0b3b3fd-5037-4973-9db2-d216382eb5c6","resolution":{"observed_at":"2026-08-15T20:45:14.375227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.356835Z","title":"An automated vision-based deep learning model for efficient detection of android malware attacks","venue":null,"work_id":"a0128d80-e6f6-4bca-a810-03c3ff63d517","year":2022},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.235670Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:965d4b8d1b804ecca3fb301ab4f577e6d0979785605a65a5c0864ac19ad5bc58","observation_id":"93163f84-f9ea-4376-bf5f-70f5f6f92126","resolution":{"observed_at":"2026-08-15T20:45:14.361259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.342566Z","title":"Machine learning with oversampling and undersampling techniques: overview study and experimental results","venue":null,"work_id":"a9d78232-50a0-41a7-b32d-913a3a3eda02","year":2020},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.240172Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:1cbbed212d04462e16dbf9839779c108a85efaeec911c663a09d3c48d39cf6f0","observation_id":"c8e1459c-2855-4902-9162-c839bc94b92c","resolution":{"observed_at":"2026-08-15T20:45:14.347102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:45:14.328260Z","title":"Handling class imbalance problem using oversampling techniques: A review","venue":null,"work_id":"fdd7c14c-c8a9-4d0e-b021-d1c1d24ea2c6","year":2017},"citing_paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:14.244387Z"},"links":{"citing_paper":"/paper/2505.12106"},"observation_digest":"sha256:cf4c14ece256095b698007e70cc9f91ba04dd9296ac48f5fcd124b5fb1deaa65","observation_id":"2af68dcb-9146-43f5-9e95-cf0d1a2d62f3","resolution":{"observed_at":"2026-08-15T20:45:14.333167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.12106","last_updated":"2025-05-17T18:19:35Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-15T20:38:45.769148Z","submitted_at":"2025-05-17T18:19:35Z","title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":60},"total_outbound_references":65},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2505.12106."}