{"as_of":"2026-08-10T17:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cf20aff583a74a974fecc715a40393e172e1937b91057474cf11cdad8c113ab1","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T12:37:27.527245Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"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/2502.07492/citation-record","integrity":"/paper/2502.07492/integrity","json":"/paper/2502.07492/citation-record.json","paper":"/paper/2502.07492"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.391518Z","title":"Structure and Interpretation of Computer Programs","venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.391518Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:6a439f8f25186987ea8afbf2f7864ed8457f0f6fb4986dcbca980afaf824baa0","observation_id":"358d62e4-c679-4074-af82-407882bd0b2d","resolution":{"observed_at":"2026-08-08T12:37:27.391518Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.395054Z","title":"Visual information extraction with Lixto","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.395054Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:cb7d02d764567f51638fa599b95d67cbdb15a9630aefa041b73210e6681644f5","observation_id":"93a0ae92-d922-4c1a-bc82-beb60b4e20d1","resolution":{"observed_at":"2026-08-08T12:37:27.395054Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.398130Z","title":"Brachman and James G","venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.398130Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:1c02c10ecc72eb2cc445a77788cd665eb55fa7e2914e5ae972976564d3b1c166","observation_id":"310b47f6-6ff9-4503-a660-de11b538afc6","resolution":{"observed_at":"2026-08-08T12:37:27.398130Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.401207Z","title":"Hypertree decompositions and tractable queries","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.401207Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:3353379ff6998339494228c9717a214314e9294dd43d371d8d73141643fb7a8d","observation_id":"a8732689-966c-41d3-821e-fc8402e0349e","resolution":{"observed_at":"2026-08-08T12:37:27.401207Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.404462Z","title":"Complexity results for nonmonotonic logics","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.404462Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:b9eead0d3ce2082a2d34422149894c05e4adc6c4c2551726ec4733e2660cfb00","observation_id":"86367f26-1ad3-4410-b48d-11655bedc72a","resolution":{"observed_at":"2026-08-08T12:37:27.404462Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.408742Z","title":"Levesque","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.408742Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:d8be57ef1aa244708dd5794a58759c8118c60c3bd09c286b77778be7fcd635a6","observation_id":"2270ea13-902e-4eb3-90f1-a1d84e704be3","resolution":{"observed_at":"2026-08-08T12:37:27.408742Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.413295Z","title":"Levesque","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.413295Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:2c51125864739ee1e6b31bc8cf3c012f0fdff440b68a0e8dec1f4fdc241ea2b5","observation_id":"d2855df6-9ed7-4c7b-b48d-e3ca00240b6d","resolution":{"observed_at":"2026-08-08T12:37:27.413295Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:37:27.417208Z","title":"On the compilability and expressive power of propositional planning formalisms","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.417208Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:a8f07652777ccec1dbc4c0382992d5cd3af97f0d812a2c083606fb7f995e2e37","observation_id":"2be4d6d3-196a-462b-910c-fd09d9f24f43","resolution":{"observed_at":"2026-08-08T12:37:27.417208Z","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":"2026-08-08T12:37:27.850872Z","title":"Malware Statistics & Trends Report","venue":null,"work_id":"9a761a70-6d2d-4bba-8ddb-8ebbce77c04f","year":2023},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.421109Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:be85b5509974d6099375c3075bfcedfb5abd9a511e1220a10efefdcd9bf07461","observation_id":"a7951105-0088-4c2b-9ab2-85c234806e08","resolution":{"observed_at":"2026-08-08T12:37:27.854835Z","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":"2026-08-08T12:37:27.840100Z","title":"Towards evaluating the robustness of neural networks","venue":null,"work_id":"62ce4ee3-3c27-47e1-85f0-e7e03526f8e4","year":2017},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.424872Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:115ea2f46bc93fab1dc0f93fe892bc3d461c3005bd1e3fdba083dc0ef976668e","observation_id":"060cbd09-1024-46c2-aa68-adad14cf1b00","resolution":{"observed_at":"2026-08-08T12:37:27.843912Z","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":"2026-08-08T12:37:27.829300Z","title":"Machine learning-enabled IoT security: Open issues and challenges under advanced persistent threats","venue":null,"work_id":"ace57656-eb3c-4b69-b0e4-b1585d43d909","year":2022},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.428509Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:ad1e47a7be5f16bdbac4407f5d22c3cd9a8f0515c1e17924f310932ec936d23c","observation_id":"4fa67157-51d2-4c1d-9657-ed499423cf40","resolution":{"observed_at":"2026-08-08T12:37:27.833308Z","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":"2026-08-08T12:37:27.818499Z","title":"Crowdstrike 2024 global threat report","venue":null,"work_id":"5ddf134e-34a4-4cd8-a449-80b0756590f9","year":2024},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.432005Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:452c47681ac0e435056403a319fbc07a951ade6dfc3808a80dcbbda21b541345","observation_id":"3909d5c8-66d5-443d-9423-68cba9699de4","resolution":{"observed_at":"2026-08-08T12:37:27.822264Z","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":"2026-08-08T12:37:27.808164Z","title":"``apt malware dataset\"","venue":null,"work_id":"0f9cc85e-d9fe-46cc-a91b-94d6475ac58b","year":2019},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.435727Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:e48f471421fe5c9f613099c688a4d766472b12eda5a02fe5e311b74b283ee82e","observation_id":"4a16c22f-f6eb-413b-a9f9-ea1b87d8d592","resolution":{"observed_at":"2026-08-08T12:37:27.811713Z","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":"2026-08-08T12:37:27.797479Z","title":"Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection","venue":null,"work_id":"c6b4274a-0732-4d95-a9bf-2b8f094e0b8d","year":2021},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.439406Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:493a3e2cdcc4d3d5da907bf90552a0c35a95d5a3884ccc349e8c31ce23573bc2","observation_id":"e7c0164c-ce51-4b34-9194-e4dc97b635c6","resolution":{"observed_at":"2026-08-08T12:37:27.801347Z","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":"2026-08-08T12:37:27.786694Z","title":"APTM alinsight: Identify and cognize apt malware based on system call information and ontology knowledge framework","venue":null,"work_id":"c5b9e9b6-1e24-43ec-b6ea-90addc811d8f","year":2021},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.442932Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:827402de952051017668a8b15d74d0e2fad59eae65bad85fa481fcfb1c828a14","observation_id":"c8a7a836-8de6-4715-a4ee-cafb7641d8f8","resolution":{"observed_at":"2026-08-08T12:37:27.790520Z","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":"2026-08-08T12:37:27.776763Z","title":"Classifying malwares for identification of author groups","venue":null,"work_id":"bf04aef3-f597-40b7-b35b-cc5a04b14387","year":2018},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.446551Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:4c8384f5767e08ac1cd773882e745bfd4401bff26e6097634386afaca12ecf5e","observation_id":"8ac64208-571c-40c8-864a-6e97b2afc4cd","resolution":{"observed_at":"2026-08-08T12:37:27.780319Z","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":"2026-08-08T12:37:27.766422Z","title":"Adversarial malware binaries: Evading deep learning for malware detection in executables","venue":null,"work_id":"665286ab-e2f8-46cb-93dc-91fa60a733c6","year":2018},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.450296Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:46c0d831865f31179a3f34f9311b4db57a9bdc5b50dc3c739937bd001d2129d4","observation_id":"4a4a54fa-2f19-49b2-96ce-415ca00b2392","resolution":{"observed_at":"2026-08-08T12:37:27.770298Z","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":"2026-08-08T12:37:27.756890Z","title":"Deep convolutional malware classifiers can learn from raw executables and labels only","venue":null,"work_id":"312afc90-93d2-481d-9833-76b815b87d81","year":2018},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.453999Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:7b9b52ea275a41c7c9336e83a9c23a2e0eae0304a9b80fff0db635cb2a40a521","observation_id":"092d7617-40e6-4ba4-9f5d-749dd73c356c","resolution":{"observed_at":"2026-08-08T12:37:27.760042Z","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":"2026-08-08T12:37:27.748323Z","title":"Deep Convolutional Malware Classifiers Can Learn from Raw Executables and Labels Only","venue":null,"work_id":"4d197eb7-eb05-4941-a46e-09e857a116c4","year":2018},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.457850Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:4b6a21f22619120c610b49e3bfeb86fa25281c1e2d0c9a764df155267c9ec179","observation_id":"b202b862-c126-4bd5-b3c6-5d444b22c500","resolution":{"observed_at":"2026-08-08T12:37:27.751267Z","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":"1802.04528","last_updated":"2019-01-10T09:21:23Z","snapshot_observed_at":"2026-08-05T16:29:23.207574Z","submitted_at":"2018-02-13T09:51:41Z","title":"Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04528","snapshot_observed_at":"2026-08-08T12:37:27.461533Z","title":"Deceiving end-to-end deep learning malware detectors using adversarial examples","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.461533Z"},"links":{"cited_paper":"/paper/1802.04528","citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:e16a8ee92abed304074377fbf612bd0d4a95af026422a293bfb58e8d3a6e0b09","observation_id":"1010d64d-fdf2-44b3-b58b-1de967e714b9","resolution":{"observed_at":"2026-08-08T12:37:27.461533Z","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":"2026-08-08T12:37:27.739457Z","title":"Malware triage for early identification of advanced persistent threat activities","venue":null,"work_id":"e27175e4-054a-411f-bca9-fa4080503dc6","year":2020},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.466002Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:22f48b09e6ae6b736fac65105088272d5133e705d43606a890eb31980c7d54ea","observation_id":"0f83a1ba-10fc-40e6-b163-d9bf64434152","resolution":{"observed_at":"2026-08-08T12:37:27.742440Z","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":"2026-08-08T12:37:27.730434Z","title":"Adversarial attacks against windows PE malware detection: A survey of the state-of-the-art","venue":null,"work_id":"285918d9-e74b-441c-aea1-3d11f0351483","year":2023},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.469618Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:0283071f719c83115adf9cdb6b2b5be7d01e253408077bac147c01b617ec46d3","observation_id":"8e04d143-39ed-473b-b517-56825de273b7","resolution":{"observed_at":"2026-08-08T12:37:27.733455Z","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":"2026-08-08T12:37:27.720161Z","title":"Functions-based CFG embedding for malware homology analysis","venue":null,"work_id":"975e252f-b55e-4a3a-bffd-b0ca32f87534","year":2019},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.473371Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:a2636c3d636b9080d25fe20a950389b5f80679e02a2fde3cb03f1d95c656f8af","observation_id":"67226d01-a48e-4d42-97af-2f9ea017e926","resolution":{"observed_at":"2026-08-08T12:37:27.723944Z","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":"2026-08-08T12:37:27.709277Z","title":"Reiter, and Saurabh Shintre","venue":null,"work_id":"7e66966a-a3b6-4e46-9078-084a8251bbe6","year":2021},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.477205Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:7cf08e0091eb6503bc2d10bc88aa88fc7accf77e9d65bd832e4ceaa81f89a38b","observation_id":"421d92d7-e7f8-462a-b1c4-0519608e0257","resolution":{"observed_at":"2026-08-08T12:37:27.712985Z","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":"2026-08-08T12:37:27.698663Z","title":"Reiter, and Mahmood Sharif","venue":null,"work_id":"636980ff-de69-4271-8682-b0d1c9983a2d","year":2023},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.481208Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:0dfe9ca7a9de8b3a58e53282001ed488b64ad0bd392a11bfa1f5dad63a12fc2b","observation_id":"653a2ed2-28b3-4dc1-8073-6b0ab233af09","resolution":{"observed_at":"2026-08-08T12:37:27.702298Z","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":"1706.06083","last_updated":"2019-09-04T18:53:10Z","snapshot_observed_at":"2026-08-07T14:27:46.872660Z","submitted_at":"2017-06-19T17:53:11Z","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06083","snapshot_observed_at":"2026-08-08T12:37:27.484899Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.484899Z"},"links":{"cited_paper":"/paper/1706.06083","citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:8d6232a955bf5361d5f9655d4c5017eef20d5292693ade08b0abb344f002e33c","observation_id":"b8712c5d-fcbc-4138-b1bd-9457be57a3e2","resolution":{"observed_at":"2026-08-08T12:37:27.484899Z","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":"2026-08-08T12:37:27.687582Z","title":"``advisory: Turla group exploits iranian apt to expand coverage of victims\"","venue":null,"work_id":"9ac4b19f-5e96-4bcd-8251-a27bc2c9c608","year":2019},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.489217Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:f1a2cc5e0a335cb1173c57d6a1f8a4c342d9d12001c71fe06428b7195bbbbda4","observation_id":"17ac83a2-54b2-4d80-b7c7-3d11032cc585","resolution":{"observed_at":"2026-08-08T12:37:27.691612Z","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":"2026-08-08T12:37:27.676908Z","title":"Intriguing Properties of Adversarial ML Attacks in the Problem Space","venue":null,"work_id":"22a021fc-8dd8-4bd8-9e90-6e0743109748","year":2020},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.493014Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:8b82f5f048f8b7f40a66a221c95389cb3f25bc9429b7b4b494a90572c9ad9c3e","observation_id":"9c868e4b-0fae-47f3-8c01-ed74916bf750","resolution":{"observed_at":"2026-08-08T12:37:27.680824Z","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":"2026-08-08T12:37:27.665709Z","title":"Malware detection by eating a whole exe","venue":null,"work_id":"bef47e04-4401-4a78-8216-8fe146b5da51","year":2018},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.497098Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:31f4dbe1549c4c8b976cec766d9a45d8d2b7491b8dc319536254143b13a0307a","observation_id":"d126aba5-2b41-478d-b9d3-0cb0b1654a6e","resolution":{"observed_at":"2026-08-08T12:37:27.669737Z","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":"2026-08-08T12:37:27.654110Z","title":"Classifying sequences of extreme length with constant memory applied to malware detection","venue":null,"work_id":"8e80bbf9-e6b0-4653-afb5-6c2a7e3da1a9","year":2021},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.501061Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:846e344ebe381fd1a9aa0744114384b84419dcc16e48e5b660ef9b820c2e7ddb","observation_id":"e8c2c990-606e-464c-9b36-a2aee8e8837a","resolution":{"observed_at":"2026-08-08T12:37:27.657916Z","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":"2026-08-08T12:37:27.642892Z","title":"Bin MLM : Binary Authorship Verification with Flow-aware Mixture-of-Shared Language Model","venue":null,"work_id":"cfdf60dc-565a-4124-b399-a04f7a281dd7","year":2022},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.505040Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:4d566e6b8bcbb80a0d63d5aa23596364345d11882930cc29c86402099d183194","observation_id":"b5d612fe-6205-4c58-a3b5-a86f3c0277d6","resolution":{"observed_at":"2026-08-08T12:37:27.646882Z","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":"2026-08-08T12:37:27.631808Z","title":"Towards efficient and effective adversarial training","venue":null,"work_id":"b12b1450-7863-49fb-a69b-2e4ce390f977","year":2021},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.508858Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:be49c93a9a7d3905ba8d8ef3eb4219ab0922b86d91d39a0f482bf90f4891a714","observation_id":"775330ea-fc21-413a-b7b4-b5ca5b322cd1","resolution":{"observed_at":"2026-08-08T12:37:27.635577Z","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":"2026-08-08T12:37:27.621076Z","title":"Mgap3: Malware group attribution based on perceiverio and polytype pre-training","venue":null,"work_id":"e209932f-a196-43d3-8cb0-e8356caf9178","year":2024},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.512509Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:b880457039c6f01983b6037dbfa07fe4a422ead6b1fc09f6fe6933108f862088","observation_id":"f3d9eebd-209a-4fc8-8183-0d3aca87bc1a","resolution":{"observed_at":"2026-08-08T12:37:27.624893Z","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":"2026-08-08T12:37:27.610281Z","title":"The Cyberthreat Report","venue":null,"work_id":"8459edd7-116e-4cdb-a58f-134b8be3d653","year":2023},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.516140Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:afff426a786012c01539d8d629204c25f93ba9fdec9d1cdfd09c78760148c7d4","observation_id":"622c338a-ba2a-4df5-bce1-7b546c17b9ce","resolution":{"observed_at":"2026-08-08T12:37:27.613887Z","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":"2026-08-08T12:37:27.518848Z","title":"Visualizing data using t-sne","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.518848Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:3de93e225b7aa3a41f9ce29c89a9138a1d2039691a9472ed799cbed0bdcc892b","observation_id":"95da9954-c24a-42e1-a727-cd42313658c1","resolution":{"observed_at":"2026-08-08T12:37:27.518848Z","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":"2026-08-08T12:37:27.593099Z","title":"VirusSign - Open Malware Database","venue":null,"work_id":"8226a2ac-b3ce-41bf-8ef1-97c02f3b75a3","year":2024},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.521615Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:8cda3b077c99bddad11e785fb440e92d2ab3f83eed76b93499365d64ade98bbe","observation_id":"4d2abef7-4dd3-4121-a6d3-291d1faae0ae","resolution":{"observed_at":"2026-08-08T12:37:27.597015Z","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":"2026-08-08T12:37:27.581241Z","title":"Fast is better than free: Revisiting adversarial training","venue":null,"work_id":"00470dbc-97e7-491e-b568-c6c966618254","year":2020},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.524390Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:6458f3d897ae71f07f548f6e3fa4d174d099fce4caf50eaf4148cdd0159ffa76","observation_id":"669f7317-5804-4894-919d-b4a6d2d433d2","resolution":{"observed_at":"2026-08-08T12:37:27.586109Z","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":"2026-08-08T12:37:27.527245Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-08T12:37:27.527245Z"},"links":{"citing_paper":"/paper/2502.07492"},"observation_digest":"sha256:701376f77b5de0e1dca3d78933f632712f5aed2ed97720e4f074201103c8b2ec","observation_id":"339f2eeb-e557-45d8-9ec5-bd0a5a0486dd","resolution":{"observed_at":"2026-08-08T12:37:27.527245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.07492","last_updated":"2025-02-15T15:36:48Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-08T23:50:18.584418Z","submitted_at":"2025-02-11T11:51:12Z","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":26},"total_outbound_references":38},"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 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2502.07492."}