{"as_of":"2026-08-10T08:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f4b85f4a3c367d6c19ae37edd374fb51200a1e0c1e9ecfd3d2da34f16c362dbd","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:28:15.269211Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"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/2505.21703/citation-record","integrity":"/paper/2505.21703/integrity","json":"/paper/2505.21703/citation-record.json","paper":"/paper/2505.21703"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:28:19.518506Z","title":"Indus- trial internet of things: Challenges, opportunities, and directions,","venue":null,"work_id":"fb8422ab-5b51-48f7-ba99-25e91ffca228","year":2018},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:12.523458Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:5a5c502f7d081176c47221948f90424df05e81ff4903d1b2366389210b4bda90","observation_id":"138c1101-e361-490f-bfb3-e2fe5a20bc7a","resolution":{"observed_at":"2026-08-07T13:28:19.546409Z","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":"2026-08-07T13:28:19.463528Z","title":"Iot practices in military applications,","venue":null,"work_id":"c6d5e9f1-e7b9-402a-a007-b624c504febf","year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:12.618267Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:5e9c9894b47523e9fc89397201435d4dd72e1af5fb191f9c39568f10f9ab3793","observation_id":"aed653e5-822f-4d72-b950-6cc6783ad28f","resolution":{"observed_at":"2026-08-07T13:28:19.508312Z","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":"2026-08-07T13:28:19.281119Z","title":"Security issues in internet of vehicles (iov): A comprehensive survey,","venue":null,"work_id":"909edd4c-ddc2-46ef-bbd4-50daf3d8b3eb","year":2023},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:12.718805Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:06b81dcf01715d35eb0226c1f0373e67269b3f6d938405d20e5ce30e97bdb248","observation_id":"209ecf5a-5f0d-47b9-8e90-75cd852cb0a1","resolution":{"observed_at":"2026-08-07T13:28:19.379889Z","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":"2026-08-07T13:28:19.142497Z","title":"An in- depth analysis of the mirai botnet,","venue":null,"work_id":"2aeafbb2-6240-4090-be95-00d9a0070a5c","year":2017},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:12.800657Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:03b557f911c90d412bf256d29696099f0338e73115cfdbde51f7ff9d31901902","observation_id":"681ecf15-d602-4416-be5e-20394101552d","resolution":{"observed_at":"2026-08-07T13:28:19.198783Z","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":"2026-08-07T13:28:19.058150Z","title":"The mirai botnet and the iot zombie armies,","venue":null,"work_id":"47d3074a-055c-44f2-8929-1a5d37706410","year":2017},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:12.860948Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:6a1ab29f6eb488f6ca89d5b9c97abdb513ef41b08fb2d5d19158b36d3147debe","observation_id":"890e8c65-a135-4d39-b649-c7c9a1b618e9","resolution":{"observed_at":"2026-08-07T13:28:19.095638Z","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":"2026-08-07T13:28:18.968471Z","title":"Mirai ddos attack against kreb- sonsecurity cost device owners $300,000,","venue":null,"work_id":"e2185516-8d08-4619-b4d0-c62e99132231","year":null},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:12.938600Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:54e21b88b9f3960f87cf43d838e692dd1f61d642b6ba44947dafd18423cfcb9e","observation_id":"cce5f642-2578-460e-94fd-ace6b0aa9a37","resolution":{"observed_at":"2026-08-07T13:28:19.005946Z","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":"2026-08-07T13:28:18.776090Z","title":"Predicting machine failures from multivariate time series: An industrial case study,","venue":null,"work_id":"608cd562-0c3f-4b44-a7a6-918ae6e413a6","year":2024},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.103426Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:9f74ea998c6cb6199e8dbe12383fca386b86e4aa1a45d480c211c797c389619b","observation_id":"5bf34290-3f58-4280-986c-fae45c8f476d","resolution":{"observed_at":"2026-08-07T13:28:18.832021Z","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":"2026-08-07T13:28:18.625158Z","title":"Deep learning for time series classification: a review,","venue":null,"work_id":"6ebd89bf-c64e-4521-818c-2813546548aa","year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.188066Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:0e3eb1e63067b3cd7e5a0c88438f005ac1c6e8591a49de14968cb0a8ce79d84d","observation_id":"d7a1ab8e-0f9a-43ea-8d2b-2f34b68ca3cd","resolution":{"observed_at":"2026-08-07T13:28:18.706591Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2026-08-07T13:28:18.488652Z","title":"Machine learning-based network vulnerability analysis of industrial internet of things,","venue":null,"work_id":"481e5878-5a03-4cb0-b96d-8f39734819b4","year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.250509Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:77c03b1089de56dec0f150d66e1f85a5fe4c5e42932f9f91fc4e4c6f9406d699","observation_id":"e5e0a78e-6e2a-4917-b01c-29e29c826ab0","resolution":{"observed_at":"2026-08-07T13:28:18.550047Z","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":"2026-08-07T13:28:18.359225Z","title":"Online and scalable unsupervised network anomaly detection method,","venue":null,"work_id":"d8b8dfbd-1907-4585-ba74-1c7352ad5d7b","year":2016},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.329093Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:1b7f739c05a0d6a225613595e0cc1b0776d11e7132bed906d665156522c6c426","observation_id":"d647ea08-4df8-47e5-9017-0cd88429e5aa","resolution":{"observed_at":"2026-08-07T13:28:18.416096Z","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":"2026-08-07T13:28:18.261128Z","title":"Detection of eavesdrop- ping attack in uav-aided wireless systems: Unsupervised learning with one-class svm and k-means clustering,","venue":null,"work_id":"75f872ac-7fe4-4283-be32-4085dd5362d5","year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.402336Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:9b8178385d52151d6f683637faf5791aec974f8ac80c14acd9e2afedfc5116e7","observation_id":"72171045-de08-4c7f-95fb-ba395e419c42","resolution":{"observed_at":"2026-08-07T13:28:18.312201Z","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":"2026-08-07T13:28:18.122255Z","title":"Deep learning for anomaly detection: Challenges, methods, and opportunities,","venue":null,"work_id":"dbcdca32-3d35-454a-9897-c63c22eb913c","year":2021},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.627735Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:d6014a5bb4c28756f4217710ade654e6b9e89daa2cb60207a170209485d7983b","observation_id":"c345ff47-9362-48a3-a723-a1e1f34bed0b","resolution":{"observed_at":"2026-08-07T13:28:18.171724Z","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":"2026-08-07T13:28:13.664670Z","title":"Anomaly detection for iot time- series data: A survey,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.664670Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:7b496df52186ee74855629c008a8d444a3694253c79ce78015ce5dc96a692f71","observation_id":"bfd8f089-b072-47ed-82c3-0e64dca99f52","resolution":{"observed_at":"2026-08-07T13:28:13.664670Z","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":"2026-08-07T13:28:18.000175Z","title":"Lstm learning with bayesian and gaussian processing for anomaly detection in industrial iot,","venue":null,"work_id":"9ec333e4-5cf9-4b0c-b030-7937e065c39f","year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.745026Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:923461da3fca85d0a74c647fefe9fd1bf49576269c1522b1080ca121c7204d37","observation_id":"db705df1-29b5-4852-b1c9-5c9aee3929c4","resolution":{"observed_at":"2026-08-07T13:28:18.036200Z","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":"2026-08-07T13:28:17.846870Z","title":"Online anomaly detection with concept drift adaptation using recurrent neural networks,","venue":null,"work_id":"3d0ea4aa-e794-4c0a-bf85-e9e15013cf76","year":2018},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.826262Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:88809043e2236a980b9f39ec7a9a2dd9aa82dcdccbc26fcda44212d0ba754b85","observation_id":"6df732dc-fb8c-49aa-b96c-9fa4df9be0c2","resolution":{"observed_at":"2026-08-07T13:28:17.927732Z","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":"2026-08-07T13:28:17.738359Z","title":"Unsupervised anomaly detection in time series using lstm-based autoencoders,","venue":null,"work_id":"0b159425-879b-4051-80cf-085afcf5d4ca","year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.902378Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:8873b97c5dfc61d2366c909531733b8f3f3fb1825de0873902ba2abc9da602a5","observation_id":"daa4bcb8-5d49-4f1f-a949-2258b2093512","resolution":{"observed_at":"2026-08-07T13:28:17.807267Z","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":"2026-08-07T13:28:17.645340Z","title":"Anomaly detection methods based on gan: a survey,","venue":null,"work_id":"7211ceab-1bf8-424a-9ef6-3aede71f656b","year":2023},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.987787Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:af1661e4d99fe1082df169bffb9a96c3db694f849259dd2f8ebc07a9889595b9","observation_id":"995d4883-60e8-4774-949d-1fe1d13a9fe0","resolution":{"observed_at":"2026-08-07T13:28:17.683652Z","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":"2026-08-07T13:28:17.537727Z","title":"Dynamic thresholding for video anomaly detection,","venue":null,"work_id":"e617e220-0fb6-42c4-aea8-ebe4caf9d61b","year":2022},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.066589Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:1783c2ec6f3d4681d927de43af6540ffce57e578d33a5adf0b026305834efc98","observation_id":"b7c1b75d-fa64-4409-b061-ef83f70dac47","resolution":{"observed_at":"2026-08-07T13:28:17.587771Z","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":"2026-08-07T13:28:17.406032Z","title":"An adversarial contrastive autoencoder for robust multivariate time series anomaly detection,","venue":null,"work_id":"f9629dd7-c491-4e50-b9a4-4271743b7579","year":2024},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.131982Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:664220244209adbe6251c68fddb647e0105266b9f8783427e0768a54586ba960","observation_id":"28d17b4b-70c6-4643-807d-a97d1c083874","resolution":{"observed_at":"2026-08-07T13:28:17.473941Z","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":"2026-08-07T13:28:17.205489Z","title":"Contrastive autoencoder for anomaly detection in multivariate time series,","venue":null,"work_id":"42444f42-7d83-4d83-b0e7-15755571bae9","year":2022},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.193768Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:8a7449b23e7dfeb65d663d2c85051d985d0f57022b884aeec64ef8f13fa4fccd","observation_id":"d8ae7883-9764-4f94-8b44-4189805a331a","resolution":{"observed_at":"2026-08-07T13:28:17.305931Z","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":"2202.07592","last_updated":"2024-09-09T16:18:54Z","snapshot_observed_at":"2026-07-06T12:38:05.979914Z","submitted_at":"2022-02-15T17:28:42Z","title":"Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data","version":3},"cited_work":{"arxiv_id":"2202.07592","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.07592","snapshot_observed_at":"2026-08-07T13:28:15.681914Z","title":"Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data","venue":"cs.LG","work_id":"b124487c-7258-4128-9b16-4da7d9ce93cb","year":2022},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.254298Z"},"links":{"cited_paper":"/paper/2202.07592","citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:9970b95bdfbd82427156a7d32eaabc1a48573cc92a9654e6957d5e9b481e2fad","observation_id":"d562ac79-9489-4e6c-857c-cb157fdcbb1b","resolution":{"observed_at":"2026-08-07T13:28:15.758623Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2301.03634","last_updated":"2023-02-23T18:12:38Z","snapshot_observed_at":"2026-07-06T14:39:46.284351Z","submitted_at":"2023-01-09T19:13:58Z","title":"Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection","version":2},"cited_work":{"arxiv_id":"2301.03634","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.03634","snapshot_observed_at":"2026-08-07T13:28:15.522020Z","title":"Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection","venue":"cs.RO","work_id":"492f21c5-3313-433b-9852-547e07f07b90","year":2023},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.358424Z"},"links":{"cited_paper":"/paper/2301.03634","citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:b46e8ace88f64b14937fcd92ccb05fcfa09804085f70f9208c880217c05d8b35","observation_id":"ae5835e7-70d0-4b55-bcda-b2fd70e940c7","resolution":{"observed_at":"2026-08-07T13:28:15.613036Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"1907.00811","last_updated":"2019-07-01T14:16:54Z","snapshot_observed_at":"2026-07-06T08:04:05.321554Z","submitted_at":"2019-07-01T14:16:54Z","title":"Location Anomalies Detection for Connected and Autonomous Vehicles","version":1},"cited_work":{"arxiv_id":"1907.00811","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.00811","snapshot_observed_at":"2026-08-07T13:28:15.363292Z","title":"Location Anomalies Detection for Connected and Autonomous Vehicles","venue":"cs.LG","work_id":"7082469b-e162-4537-93df-f8fcd00a14c2","year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.467325Z"},"links":{"cited_paper":"/paper/1907.00811","citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:a6a7a3d0d29cc13df0fec8d766f1ee0226d3f96ed9b3be04db24d8d1323a4ed9","observation_id":"d7f2dc2e-c8e4-4464-85e9-ac8250e23732","resolution":{"observed_at":"2026-08-07T13:28:15.444788Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-07T13:28:17.004269Z","title":"Xai-ads: An explainable artificial intelligence framework for enhancing anomaly detection in autonomous driving systems,","venue":null,"work_id":"0219feaa-df0f-4cbc-bf47-66145fc0c2a2","year":2024},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.548907Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:2e0a934a168aae43cee5c01d984693f2f26a5b3bf7b8348803ac2cfe622e65ae","observation_id":"7f34d48f-64a5-48ba-993d-5761f243f7dc","resolution":{"observed_at":"2026-08-07T13:28:17.092656Z","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":"2026-08-07T13:28:16.794546Z","title":"Vanet network traffic anomaly detection using gru- based deep learning model,","venue":null,"work_id":"72ff81a9-bed9-4c59-b2c0-3d9a809c56e0","year":2024},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.599506Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:b7c22078cd180b393fa758cffe4dfe50977fee468526f55347c60fd991eb3e10","observation_id":"fda0a23d-676d-478d-a60b-167d3f0c3abd","resolution":{"observed_at":"2026-08-07T13:28:16.883470Z","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":"2026-08-07T13:28:16.643053Z","title":"Securing vanets: Multi-objective intrusion detection with variational autoencoders,","venue":null,"work_id":"0c74a10d-b5e4-4bff-bc3b-14b9d532e59c","year":2024},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.671315Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:84d01a730f51a4c71b540b869cd5d06c08bdaccd6f9eb55fbb08ef335ea8c48a","observation_id":"0ba14ff8-e35d-4f59-b2f9-1bf76de8ee42","resolution":{"observed_at":"2026-08-07T13:28:16.696933Z","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":"2201.03898","last_updated":"2022-01-11T11:55:32Z","snapshot_observed_at":"2026-08-09T16:43:30.180116Z","submitted_at":"2022-01-11T11:55:32Z","title":"An Introduction to Autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.03898","snapshot_observed_at":"2026-08-07T13:28:14.767436Z","title":"An introduction to autoencoders,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.767436Z"},"links":{"cited_paper":"/paper/2201.03898","citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:d4d8ff55bb4e1b879eb88f23adf479361fc971f783f37f3510a9e4d9913509f1","observation_id":"be352566-116f-499d-88b0-9a1a8276b35b","resolution":{"observed_at":"2026-08-07T13:28:14.767436Z","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":"2026-08-07T13:28:16.539376Z","title":"Triplet loss with multistage outlier suppression and class-pair margins for facial expres- sion recognition,","venue":null,"work_id":"0d87c39e-6057-4149-b280-402657578cda","year":2022},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.816199Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:4b3787edbbcfeae407472633752e52813deeaeb42691c5812aff6f7e76c88c93","observation_id":"733cefb3-14fc-40c2-8659-b3d33158fdf5","resolution":{"observed_at":"2026-08-07T13:28:16.588272Z","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":"2026-08-07T13:28:16.420365Z","title":"Two-stage method based on triplet margin loss for pig face recognition,","venue":null,"work_id":"e9f41619-df46-4f3b-b975-2ab599664bd0","year":2022},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.891777Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:bb28cabe54786e83e62f8916b4609f176dc96e450304a56900127482a715152b","observation_id":"4efb7d90-4d89-4dd5-a13c-5ada1090e1d5","resolution":{"observed_at":"2026-08-07T13:28:16.472517Z","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":"2026-08-07T13:28:16.220060Z","title":"Facenet: A unified embed- ding for face recognition and clustering,","venue":null,"work_id":"7c502f54-b548-4c82-8db8-fb6a1dfaddf1","year":2015},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:14.982318Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:14bb1a77882b84effe13f9685c58e3acaf71da485299ddc76caba65e8cf79468","observation_id":"9bd65fcc-bc6e-46ad-8994-46df56ab5ebf","resolution":{"observed_at":"2026-08-07T13:28:16.332987Z","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":"2026-08-07T13:28:16.070949Z","title":"Smote: synthetic minority over-sampling technique,","venue":null,"work_id":"ca6b3dbb-68b7-4d83-947c-cc99a89df887","year":2002},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:15.070877Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:6f720e016b1a8e67ec96996898d20bf9a93929fcf96deb34e2def9c58bfab277","observation_id":"75cf4377-12f2-43cf-a5ca-a68cc41b82d5","resolution":{"observed_at":"2026-08-07T13:28:16.136450Z","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":"2026-08-07T13:28:15.137452Z","title":"Pyod: A python toolbox for scalable outlier detection,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:15.137452Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:86533e07e96c730666493977a7b48a6475ea6b91361a4e532df4f24ed46b44a2","observation_id":"313826fe-f875-4710-9337-f4a786a03749","resolution":{"observed_at":"2026-08-07T13:28:15.137452Z","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":"2026-08-07T13:28:15.946981Z","title":"Deep one-class classification,","venue":null,"work_id":"1fa52737-3653-4036-a5f1-66cc43f88914","year":2018},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:15.201184Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:79a31b4f82c708875aa302b37fa1af7cb2d604ccb5d2964595b8f3984375d90f","observation_id":"133bf1a6-832c-4e14-9fc6-a2c74b9ee6fd","resolution":{"observed_at":"2026-08-07T13:28:16.006133Z","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":"2026-08-07T13:28:15.813893Z","title":null,"venue":null,"work_id":"99b716ba-4a11-471f-ae9a-72df761a006e","year":2017},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:15.269211Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:9a1cb22d2a9c4d5da2c00b8c6a9f2c94b82267e17399c60324e5092baa16f941","observation_id":"b7cd5468-0ab8-4c42-8ddb-615dc7f602f4","resolution":{"observed_at":"2026-08-07T13:28:15.878406Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-07T13:28:18.889639Z","title":"Available: https://www.zdnet.com/article/ mirai-botnet-attack-against-krebsonsecurity-cost-device-owners-300000/","venue":null,"work_id":"bfff0431-0b8a-4077-8f91-d889281a1cbc","year":null},"citing_paper":{"arxiv_id":"2505.21703","last_updated":"2025-05-27T19:40:57Z","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:13.017081Z"},"links":{"citing_paper":"/paper/2505.21703"},"observation_digest":"sha256:56b60316228ed6aaf38bec1cb1968de0253ff9ad760ee7c3a8747c3b1977cdc7","observation_id":"6215a0da-382d-411d-9d32-be5a67c638d8","resolution":{"observed_at":"2026-08-07T13:28:18.922935Z","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":"2505.21703","last_updated":"2025-05-27T19:40:57Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-10T05:01:24.703439Z","submitted_at":"2025-05-27T19:40:57Z","title":"A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":3,"verified_fuzzy":27},"total_outbound_references":35},"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 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.21703."}