{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RNMW7ARQ2LBGMEEHCCFJ7VWJJH","short_pith_number":"pith:RNMW7ARQ","schema_version":"1.0","canonical_sha256":"8b596f8230d2c2661087108a9fd6c949d694840c779a77884a46f445a02d6373","source":{"kind":"arxiv","id":"2507.08177","version":1},"attestation_state":"computed","paper":{"title":"Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.NE"],"primary_cat":"cs.LG","authors_text":"Anjali Kaushik, Arun Vignesh Malarkkan, Dongjie Wang, Haoyue Bai, Xinyuan Wang, Yanjie Fu","submitted_at":"2025-07-10T21:19:28Z","abstract_excerpt":"As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio-Temporal Anomaly detection plays an important role in ensuring system security and operational integrity. However, current data-driven approaches, largely driven by black-box deep learning, face challenges in interpretability, adaptability to distribution shifts, and robustness under evolving system dynamics. In this paper, we advocate for a causal learning perspective to advance anomaly detection in spatially distr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.08177","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-10T21:19:28Z","cross_cats_sorted":["cs.AI","cs.ET","cs.NE"],"title_canon_sha256":"72d0c2b56468cae7a476ec905c99659865f2e8c216b071817860822393bc5d00","abstract_canon_sha256":"3e6796ccf314b4f22d1ac6f76d03ce62e849295916bd30d134720842f3d3bc48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:18.652728Z","signature_b64":"q3zPww8h0MG/esiemrR5lMmGAnIx7nGWlpocPjkA9MzaXlFUN+NngEJ4NT2SvHnJO7c2uEmECfY2Lct5OtvGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b596f8230d2c2661087108a9fd6c949d694840c779a77884a46f445a02d6373","last_reissued_at":"2026-07-05T11:35:18.652125Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:18.652125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.NE"],"primary_cat":"cs.LG","authors_text":"Anjali Kaushik, Arun Vignesh Malarkkan, Dongjie Wang, Haoyue Bai, Xinyuan Wang, Yanjie Fu","submitted_at":"2025-07-10T21:19:28Z","abstract_excerpt":"As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio-Temporal Anomaly detection plays an important role in ensuring system security and operational integrity. However, current data-driven approaches, largely driven by black-box deep learning, face challenges in interpretability, adaptability to distribution shifts, and robustness under evolving system dynamics. In this paper, we advocate for a causal learning perspective to advance anomaly detection in spatially distr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08177","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.08177/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.08177","created_at":"2026-07-05T11:35:18.652196+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.08177v1","created_at":"2026-07-05T11:35:18.652196+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08177","created_at":"2026-07-05T11:35:18.652196+00:00"},{"alias_kind":"pith_short_12","alias_value":"RNMW7ARQ2LBG","created_at":"2026-07-05T11:35:18.652196+00:00"},{"alias_kind":"pith_short_16","alias_value":"RNMW7ARQ2LBGMEEH","created_at":"2026-07-05T11:35:18.652196+00:00"},{"alias_kind":"pith_short_8","alias_value":"RNMW7ARQ","created_at":"2026-07-05T11:35:18.652196+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00693","citing_title":"DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH","json":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH.json","graph_json":"https://pith.science/api/pith-number/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/graph.json","events_json":"https://pith.science/api/pith-number/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/events.json","paper":"https://pith.science/paper/RNMW7ARQ"},"agent_actions":{"view_html":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH","download_json":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH.json","view_paper":"https://pith.science/paper/RNMW7ARQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.08177&json=true","fetch_graph":"https://pith.science/api/pith-number/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/graph.json","fetch_events":"https://pith.science/api/pith-number/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/action/storage_attestation","attest_author":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/action/author_attestation","sign_citation":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/action/citation_signature","submit_replication":"https://pith.science/pith/RNMW7ARQ2LBGMEEHCCFJ7VWJJH/action/replication_record"}},"created_at":"2026-07-05T11:35:18.652196+00:00","updated_at":"2026-07-05T11:35:18.652196+00:00"}