{"as_of":"2026-08-10T08:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3ab9e6fc4fe7704957848717abb5b07882fa954dc6451b76a8195f7d7d0465ae","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:24:28.175056Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.00348/citation-record","integrity":"/paper/2507.00348/integrity","json":"/paper/2507.00348/citation-record.json","paper":"/paper/2507.00348"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:24:35.060599Z","title":"Learning under concept drift: A review,","venue":null,"work_id":"b848b06d-842a-47ac-90d9-0b0d1779f19c","year":2018},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:22.506950Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:4d45bfffb0158524cf8ebae0e32f8e4bb9cd0f823a936d358aa035244cb3cccc","observation_id":"daf43dc6-58ab-4def-b233-a60960efa9dd","resolution":{"observed_at":"2026-08-06T21:24:35.132020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:34.942287Z","title":"Malware statistics & trends report,","venue":null,"work_id":"fdacb405-4df2-4f73-b53b-ee8af032babf","year":2024},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:22.572347Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:cdcf3c8f27d0cc8ff31b0ac5b24eaa7d1a9ff15536f07221c4167d504d83518b","observation_id":"3da4f227-57c4-4c6b-84e3-c7d90c2d6040","resolution":{"observed_at":"2026-08-06T21:24:34.970006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:34.793358Z","title":"Tesseract: Eliminating experimental bias in malware classification across space and time,","venue":null,"work_id":"39226456-59b1-43ec-b7fc-2c45b8a0916f","year":2019},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:22.683622Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:b818ab269148747b9a2c2b18e583b9f5eed620240d7412f2b31d552fc0ac3709","observation_id":"f26d1dfc-2f30-49c5-8475-a0c20670d5e7","resolution":{"observed_at":"2026-08-06T21:24:34.847594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:34.644849Z","title":"Transcend: Detecting concept drift in malware classification models,","venue":null,"work_id":"d6817e67-5da7-43f2-b0b9-15783d474cab","year":2017},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:22.795328Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:4797135bef59aa42a3762bc33ff1cc82523490c5e34682e74d6de58b9a453718","observation_id":"2f0874da-a30e-4c0e-863e-ce0b4f0d7c7a","resolution":{"observed_at":"2026-08-06T21:24:34.693112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:34.493950Z","title":"Towards open set deep networks,","venue":null,"work_id":"d490eb9b-fd77-4d8b-a46a-c905a2d825df","year":2016},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:22.909716Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:791f1e4658e25615444b52a6707c35f2fe73deaa1000c793ea1600256868b256","observation_id":"23677695-f2e4-4ca7-8297-757b18ffb408","resolution":{"observed_at":"2026-08-06T21:24:34.593675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:34.389258Z","title":"A simple unified frame- work for detecting out-of-distribution samples and adversarial attacks,","venue":null,"work_id":"cbe1bd4d-44fc-4a55-9f43-97e6b1344791","year":2018},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.034599Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:09c7005d931edace6b955e178f2b06e4f229db3e8e752208efa301a2b0d8143c","observation_id":"cb39a577-d71f-4b32-b74c-dd3091daa292","resolution":{"observed_at":"2026-08-06T21:24:34.448546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:34.196401Z","title":"Android malware detection: Mission accomplished? a review of open challenges and future per- spectives,","venue":null,"work_id":"dbaad855-98e9-4440-a46a-39d42de49408","year":2023},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.158133Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:26dca0b72836207606b269b226a158b50e8fb01651c4406fd45ff218d517ee87","observation_id":"fa35d2d4-7afc-49a6-906b-9e59bec608a4","resolution":{"observed_at":"2026-08-06T21:24:34.293761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:33.940121Z","title":"Novel feature extraction, selection and fusion for effective malware family classification,","venue":null,"work_id":"3f85286c-5004-4264-a487-5bc4a550663f","year":2016},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.302066Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:7730d0f6bf0848efecb262fd484f4e6e49fee7e2f02d7b817e8d6dc397e682e1","observation_id":"77dab403-ddef-4a03-931b-3cda12b7128a","resolution":{"observed_at":"2026-08-06T21:24:34.047124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:33.765780Z","title":"Malware detection based on mining api calls,","venue":null,"work_id":"610e3967-df86-4ded-b49f-c86c26828c22","year":2010},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.443732Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:0b40928c58054061431b1df7119f4e89cfa834cf1c34f2e7bd0f159c8f3ef2f2","observation_id":"e47cc1cf-0881-47bb-8a70-5f03cc4ea9ca","resolution":{"observed_at":"2026-08-06T21:24:33.861866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:33.523709Z","title":"Byte level n–gram analysis for malware detection,","venue":null,"work_id":"9b270e40-82a4-4c5e-ac58-04c04187491e","year":2011},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.543286Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:f13fcd511260670d235ec275b3fa06d0cb652b467cb078f1e0326a050ec23230","observation_id":"3e97df00-b435-42be-af73-bc463c1a8b90","resolution":{"observed_at":"2026-08-06T21:24:33.641254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:33.369920Z","title":"Malware detection and classifica- tion based on n-grams attribute similarity,","venue":null,"work_id":"b3e6bf21-c3e9-4e02-9da5-75994b7e356d","year":2017},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.695974Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:b4d326dc6940d671cf058b635ece28e55964d3cc386feeb4e35a6736d2ee4bf8","observation_id":"c15a45e1-467e-4c3e-b9ef-24e62d8791cc","resolution":{"observed_at":"2026-08-06T21:24:33.443859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:33.189372Z","title":"Deep android malware detection,","venue":null,"work_id":"70237bf8-3501-4d0b-a942-d3fb0bf266cd","year":2017},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.819327Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:a79273c1218e939f2227434600b099e9e8d2ac900adc5f8f036d1b8a530b48d5","observation_id":"6d42dfca-8a08-411e-8b3c-3779d8b3f05f","resolution":{"observed_at":"2026-08-06T21:24:33.261688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:32.979802Z","title":"Sequential op- code embedding-based malware detection method,","venue":null,"work_id":"348d7695-2af9-4661-b18b-39744da475cb","year":2022},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:23.964083Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:6f39718442e3a5d2ccbc6c2f4e3b3fc9a8513109a0693c1498b6bd8444986077","observation_id":"53601bd8-001b-4d32-8376-6729ff5aaca1","resolution":{"observed_at":"2026-08-06T21:24:33.082834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:32.799104Z","title":"Malware detection based on deep learn- ing algorithm,","venue":null,"work_id":"dceeab2c-d1b9-44a1-b8f9-80fefe2ee5b4","year":2019},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:24.077092Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:dc1ea16e2a14307f2d1bd7de325a1154561237414a683f949dc8afdc540db7d3","observation_id":"4273bd73-ceae-4da6-89e5-4cd88a616410","resolution":{"observed_at":"2026-08-06T21:24:32.876383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:32.608701Z","title":"Drebin: Effective and explainable detection of android malware in your pocket.,","venue":null,"work_id":"42e842e5-b82e-4bf1-9a2a-b13274bf2e11","year":2014},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:24.164039Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:c655b3febf26216a868db72d63bf851a65ddf76077ccc03f9d63f6fffb13390a","observation_id":"85564bd2-3c10-4cf7-a186-6a084c47fe9a","resolution":{"observed_at":"2026-08-06T21:24:32.684049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.04637","last_updated":"2018-04-16T20:43:33Z","snapshot_observed_at":"2026-07-06T06:33:11.138971Z","submitted_at":"2018-04-12T17:23:56Z","title":"EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.04637","snapshot_observed_at":"2026-08-06T21:24:24.305129Z","title":"Ember: An open dataset for training static pe malware machine learning models,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:24.305129Z"},"links":{"cited_paper":"/paper/1804.04637","citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:b2b04084d101adbb72c3fe1d1164ed0f509d8360fb116c985284447a1e5e159a","observation_id":"10bb0485-9b61-44cd-aa91-09c322fd179c","resolution":{"observed_at":"2026-08-06T21:24:24.305129Z","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":"2026-08-06T21:24:32.480525Z","title":"Bodmas: An open dataset for learning based temporal analysis of pe malware,","venue":null,"work_id":"26341ec8-74b6-4bc5-aa50-de34dbbb996a","year":2021},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:24.442372Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:c6ee5ff2630b213e4a3c3e5a7885588f12a92d566d76671ea9a6ea5315a3b1c6","observation_id":"f1f13758-ddc6-4e87-a011-64b1ff7ab7f8","resolution":{"observed_at":"2026-08-06T21:24:32.546103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:32.341348Z","title":"Maar: Robust features to detect malicious activity based on api calls, their arguments and return values,","venue":null,"work_id":"574ca0be-6a8c-4d9f-a886-68440810a82e","year":2017},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:24.592688Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:93227a0c40c22b110e22ceb5129c8bf7442a726a3a6ec6d6d433a6180308ec91","observation_id":"866af7a4-b19a-49d8-92aa-c3e4d75c0f7a","resolution":{"observed_at":"2026-08-06T21:24:32.389295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:32.206062Z","title":"Malware detection and classification based on extraction of api sequences,","venue":null,"work_id":"7136b63e-2f34-49ae-818c-861391da0150","year":2014},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:24.732030Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:5d16ec424eb79b9a9f15ca282101fa03972df6b94ba54e4ffdce66abf34b72b7","observation_id":"8ea392d8-a515-40d4-96b5-a8eee94f0946","resolution":{"observed_at":"2026-08-06T21:24:32.269841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:32.054487Z","title":"Detecting obfus- cated malware using reduced opcode set and optimised runtime trace,","venue":null,"work_id":"086a7383-36f9-4c19-8445-394806837317","year":2016},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:24.900015Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:323f9e697112c983d0a430b68a74694a10d2978b24410a306541f1b60fd48cc0","observation_id":"c66656f8-6d30-4c4d-a886-0817ba93704a","resolution":{"observed_at":"2026-08-06T21:24:32.115445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.918148Z","title":"Network malware classification comparison using dpi and flow packet headers,","venue":null,"work_id":"9a5fd868-0b6a-4ca3-956d-f9d57a69274c","year":2016},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.020941Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:8b9d716df6481cb8c6758d794bef9e34e83c562db0c5ef5b7e6f5620e3968d7b","observation_id":"02ec6da5-3505-4d7e-8bff-dba41a53271c","resolution":{"observed_at":"2026-08-06T21:24:31.979431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.726970Z","title":"Malicious software classification using transfer learning of resnet-50 deep neural network,","venue":null,"work_id":"889aefbb-509f-46eb-9c75-17c62d5ca9b4","year":2017},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.126684Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:21c9e741704dc3de429b84ef5feca953ff5e60d9087a7cb1ebafea2d57f0a6e2","observation_id":"f5fc26f6-bc4c-4195-b172-239b17fec7a4","resolution":{"observed_at":"2026-08-06T21:24:31.830242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.07737","last_updated":"2017-11-21T15:35:07Z","snapshot_observed_at":"2026-08-04T09:19:45.912586Z","submitted_at":"2017-03-22T16:34:29Z","title":"In Defense of the Triplet Loss for Person Re-Identification","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.07737","snapshot_observed_at":"2026-08-06T21:24:25.278451Z","title":"In defense of the triplet loss for person re-identification,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.278451Z"},"links":{"cited_paper":"/paper/1703.07737","citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:317d056eb35f1a1d53cdae228de99b878e7afce48c9701969e85f689d73ff08f","observation_id":"3a3d06bd-593f-456d-82f0-b4d6e3c7ea24","resolution":{"observed_at":"2026-08-06T21:24:25.278451Z","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":"2026-08-06T21:24:31.575152Z","title":"Triplet loss in siamese network for object tracking,","venue":null,"work_id":"d2a17873-c79a-400f-bc6d-76cfeb8dac22","year":2018},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.397796Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:de0f35e82ca16a323ca16c93cabc0419051474e67883e818b7d766d82adf1d75","observation_id":"e916bd8d-fa39-4ede-83d1-f74332c9bb63","resolution":{"observed_at":"2026-08-06T21:24:31.641987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.511452Z","title":"A zero-shot deep metric learning approach to brain–computer interfaces for image retrieval,","venue":null,"work_id":"d2934a68-a38b-4d2c-9a37-e1ef62c0434e","year":2022},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.524819Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:d1f5aa066b84257748ac93eb7eafd95b49698abb5da7b77ed1ad54c83f27f4d6","observation_id":"81b39c97-d5de-4ab7-b662-5f2dc19e54cb","resolution":{"observed_at":"2026-08-06T21:24:31.536173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-06T21:24:25.662754Z","title":"Representation learning with contrastive predictive coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.662754Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:0f27bed74c8543efa3a680849c917b74fb13bebbb2f478a728c06acbc437b497","observation_id":"37d6835d-ccbc-4ee4-8385-8de4f5c33da0","resolution":{"observed_at":"2026-08-06T21:24:25.662754Z","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":"2026-08-06T21:24:31.453576Z","title":"Metric learning- based multimodal audio-visual emotion recognition,","venue":null,"work_id":"2fec5f09-dac2-4730-b98b-762f15e7267d","year":2019},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.776825Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:09295ba6f05e03116a059bd5258d2a1ed11e7963fb6c7dadc99e787ce04b00c6","observation_id":"733a8fd9-329c-48fc-8347-9c2287a340ae","resolution":{"observed_at":"2026-08-06T21:24:31.477020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.11982","last_updated":"2020-04-24T05:52:11Z","snapshot_observed_at":"2026-08-07T01:23:56.303542Z","submitted_at":"2020-03-26T15:43:10Z","title":"In defence of metric learning for speaker recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.11982","snapshot_observed_at":"2026-08-06T21:24:25.971627Z","title":"In defence of metric learning for speaker recognition,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:25.971627Z"},"links":{"cited_paper":"/paper/2003.11982","citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:82f6d4db532d8a613b76ed9fd83d3ff6e73aae36282ac51efc846b1af1f65081","observation_id":"e33bdbb9-9350-4ae5-819e-86f39e2d4765","resolution":{"observed_at":"2026-08-06T21:24:25.971627Z","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":"2026-08-06T21:24:31.394300Z","title":"Multi-instance multi- label distance metric learning for genome-wide protein func- tion prediction,","venue":null,"work_id":"b4aba63c-78bc-476a-915d-ad75cba36cf4","year":2016},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.132677Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:48b8269573674e90fa8991def3eae4304795b22ae46587e453250b5575c26ca5","observation_id":"560dc32d-29d3-4e5f-8ced-b623fd0ae49b","resolution":{"observed_at":"2026-08-06T21:24:31.416088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.334081Z","title":"A novel drug repositioning approach based on collaborative metric learning,","venue":null,"work_id":"19f4fd85-4944-4ec4-96d6-9247b5c7cbac","year":2019},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.242253Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:2160d313d751a7733202169e7df60bf84ababdb3ff34974e033238127ee764b7","observation_id":"0908475a-f05f-4880-a916-aca98aeb46fe","resolution":{"observed_at":"2026-08-06T21:24:31.359283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.276744Z","title":"Contrastive learning for robust android malware familial classification,","venue":null,"work_id":"c2978179-24ae-4b7f-b19f-d1af43c93809","year":2022},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.337027Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:9517e7ed1d99015a93041ec385ca8242ef28f5f0c9981b55707fb0a073e14eb9","observation_id":"f42a27c1-9b3e-4b1c-8896-cc76654b3e98","resolution":{"observed_at":"2026-08-06T21:24:31.304388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.216520Z","title":"Application of distance metric learning to automated malware detection,","venue":null,"work_id":"45dcb7db-1de4-422a-84f3-12a2900b7c4c","year":2021},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.444889Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:1f4e8069025fde33f3aa1cbfdcd15695372bfc73e066274dd9d61503bc11cddd","observation_id":"eb2bae39-e00a-4099-930a-f9412e3edd64","resolution":{"observed_at":"2026-08-06T21:24:31.244624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.161805Z","title":"Fewm- hgcl: Few-shot malware variants detection via heterogeneous graph contrastive learning,","venue":null,"work_id":"299a8fae-9051-4b27-bb28-1c9d770ee109","year":2022},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.555216Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:9bcb5db71209336146a6b0510bf26a0a8dd917f55b6ead6edf28d308a4a19813","observation_id":"d8b85695-ccce-421c-a081-26520e140d42","resolution":{"observed_at":"2026-08-06T21:24:31.184722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:31.102314Z","title":"Autoencoder- based deep metric learning for network intrusion detection,","venue":null,"work_id":"5bfdbeca-4cd8-4a98-862c-8544760e6c76","year":2021},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.653212Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:14ccd01b3ec75adf8ad5d20dce6e5678f1bf7d47842cd4555cc9ade09b39b404","observation_id":"54e9a541-f18a-4470-bfe7-d581875df7fa","resolution":{"observed_at":"2026-08-06T21:24:31.122968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:30.926086Z","title":"Tracking concept drift in malware families,","venue":null,"work_id":"3680fe5c-c5af-4393-aa36-221ae2805188","year":2012},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.752232Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:62ba58086b4f163526184c5a50eb98f6649cf91d61acdcc57eff80e1bc4e5c9b","observation_id":"7b69e132-74b4-4976-b192-310f086fdcf0","resolution":{"observed_at":"2026-08-06T21:24:31.060646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:30.625737Z","title":"Transcending transcend: Revisiting malware classification in the presence of concept drift,","venue":null,"work_id":"d2ff5602-95cb-4505-8447-559b2b5edc39","year":2022},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.860210Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:ea1cf2bbc932c9dcac3b664c2446d5d5b9fe75f6e0e7c1f6e5198fa08d99c4ba","observation_id":"12fe764d-e597-4288-91ae-b1aa612b2fc1","resolution":{"observed_at":"2026-08-06T21:24:30.762179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:30.276599Z","title":"Cade: Detecting and explaining concept drift samples for security applications,","venue":null,"work_id":"4f95e884-f69f-4923-8b86-93bea605eb05","year":2021},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:26.980269Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:a01f566351fa4b52fe2b8a3b30062ae697dee64fa23c89c620e392f735858996","observation_id":"0cc4d6c2-177a-4dd7-89ae-737fae0d4f1c","resolution":{"observed_at":"2026-08-06T21:24:30.416226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:27.129629Z","title":"Insomnia: Towards concept-drift robustness in network intrusion detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:27.129629Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:26e2a444c1f10a3490ba3cb287119681801b4470f117059c721f0f857c3dd906","observation_id":"31fa12e7-0792-40f0-9608-eec5d7f5c464","resolution":{"observed_at":"2026-08-06T21:24:27.129629Z","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":"2026-08-06T21:24:29.836886Z","title":"Temporal analysis of dis- tribution shifts in malware classification for digital forensics,","venue":null,"work_id":"c0a2ab0c-38ab-4465-a553-b3140e9784b2","year":2023},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:27.256083Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:74a155f9a19a3b1c3c4b4af299e89da22d17fadd7dd04ce565084988376e11df","observation_id":"13dbe3f4-b540-4c68-b66a-fbd8b5f54305","resolution":{"observed_at":"2026-08-06T21:24:30.041048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:29.575586Z","title":"Deep metric learning: A survey,","venue":null,"work_id":"e589dbb6-74a6-40e6-8def-23f84011bb4d","year":2019},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:27.408873Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:5212da75f6324140e2c1275ed4c3e4c75f4ad95d1aba7fbb664eedb00d3be301","observation_id":"3319fb0d-3f8c-4dcf-8346-9fb77e45985d","resolution":{"observed_at":"2026-08-06T21:24:29.686585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:29.339458Z","title":"The curse (s) of dimension- ality,","venue":null,"work_id":"7e9c472f-0b5d-4308-ac51-0a8efe954d38","year":2018},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:27.540692Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:f83c55cfbb5799820975e59085cf90fc0820726a80d22e9edac60169b0d1e9bf","observation_id":"ee246fe0-6992-4398-af4d-3911426eb9a5","resolution":{"observed_at":"2026-08-06T21:24:29.446959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:29.149428Z","title":"Dbscan revisited, revisited: Why and how you should (still) use dbscan,","venue":null,"work_id":"1d7453f7-ecf4-4894-92c2-071fadeeb7de","year":2017},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:27.645378Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:119631a2d5f1d2acd7bf002f2e7d087523d5e0a4350aa3a26594536e5182ae6d","observation_id":"7587d0e6-c042-4ff0-bd24-93ad73fd57bf","resolution":{"observed_at":"2026-08-06T21:24:29.226658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:28.941368Z","title":"The k-means algo- rithm: A comprehensive survey and performance evaluation,","venue":null,"work_id":"21121095-e332-4252-9526-600e5dcaf25d","year":2020},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:27.750553Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:40de0e4eefb7f0df6fa60c65a7b42245b0b41e961e4c3b4d97aad304808b968e","observation_id":"03e51e84-db58-4670-86b2-f44eb5579e92","resolution":{"observed_at":"2026-08-06T21:24:29.052189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:28.718279Z","title":"A density-based algorithm for discovering clusters in large spatial databases with noise,","venue":null,"work_id":"a5eba373-5066-47b5-a573-00c642551345","year":1996},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:27.887440Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:346c995c4b8ae451f43e69dc564d79310d419ab6aa6742aa814b7cb9741ebbf4","observation_id":"da42a5c0-e3fa-4b0f-b718-1fc7ca4b28a3","resolution":{"observed_at":"2026-08-06T21:24:28.848425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:28.520026Z","title":"Fesa: Feature selection architecture for ransomware detection under concept drift,","venue":null,"work_id":"e3ff770e-c334-48c1-a82b-b1c0119cabc1","year":2022},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:28.049585Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:a6785e73f320c8d9c22b49bb0134fb56961a6774b16c2dfa84ab3fc04f76c281","observation_id":"4107eb0c-a581-47c3-8720-e4996b8d5bd0","resolution":{"observed_at":"2026-08-06T21:24:28.598824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T21:24:28.345294Z","title":"Visualizing data using t- sne.,","venue":null,"work_id":"802e95d7-9981-4004-924b-02390a11a841","year":2008},"citing_paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:24:28.175056Z"},"links":{"citing_paper":"/paper/2507.00348"},"observation_digest":"sha256:b782c1227c576453d88af836f432bedfda8c56fde4bbf4aaff956482141d364c","observation_id":"ceafdbab-16ce-4eb3-b748-ce7f24bb66c1","resolution":{"observed_at":"2026-08-06T21:24:28.428957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.00348","last_updated":"2025-07-01T00:55:00Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-06T21:15:50.762158Z","submitted_at":"2025-07-01T00:55:00Z","title":"Addressing malware family concept drift with triplet autoencoder"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":41},"total_outbound_references":46},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.00348."}