{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RNYUQZ4DBIKLSM7I3KVRMFA3MA","short_pith_number":"pith:RNYUQZ4D","schema_version":"1.0","canonical_sha256":"8b714867830a14b933e8daab16141b60107d48968ef9f587e689e14280b02174","source":{"kind":"arxiv","id":"2410.22967","version":5},"attestation_state":"computed","paper":{"title":"Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Jin Wang, Yachao Yuan, Yu Huang","submitted_at":"2024-10-30T12:26:02Z","abstract_excerpt":"The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that can adapt to evolving traffic pattern is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. In this paper, we design Adaptive NAD, an online and self-Adaptive unsupervised Network Anomaly Detection framework for security domains. A two-layer anomaly detection strategy is proposed to generate reliable high-confidence pseudo-labels. Then"},"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":"2410.22967","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-30T12:26:02Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"ebfdb354fd895927fd2abcaa7b3dac1361fbca34bd33a9c17bee87cea942031e","abstract_canon_sha256":"1061d398b80b307efab78a6c2ca75b48f840042efdafed7520c98a1cd16bdb9d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:39.183850Z","signature_b64":"Tx/+/CHyGYVHE3CF/UAuVtipeTdTLHYaeV3btrBCOv3+Lx8m6RFPyG+T9SpIuj7ooVAN4m1ENe4iMKQjILkPDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b714867830a14b933e8daab16141b60107d48968ef9f587e689e14280b02174","last_reissued_at":"2026-06-01T01:03:39.183274Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:39.183274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Jin Wang, Yachao Yuan, Yu Huang","submitted_at":"2024-10-30T12:26:02Z","abstract_excerpt":"The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that can adapt to evolving traffic pattern is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. In this paper, we design Adaptive NAD, an online and self-Adaptive unsupervised Network Anomaly Detection framework for security domains. A two-layer anomaly detection strategy is proposed to generate reliable high-confidence pseudo-labels. Then"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.22967","kind":"arxiv","version":5},"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/2410.22967/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":"2410.22967","created_at":"2026-06-01T01:03:39.183382+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.22967v5","created_at":"2026-06-01T01:03:39.183382+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.22967","created_at":"2026-06-01T01:03:39.183382+00:00"},{"alias_kind":"pith_short_12","alias_value":"RNYUQZ4DBIKL","created_at":"2026-06-01T01:03:39.183382+00:00"},{"alias_kind":"pith_short_16","alias_value":"RNYUQZ4DBIKLSM7I","created_at":"2026-06-01T01:03:39.183382+00:00"},{"alias_kind":"pith_short_8","alias_value":"RNYUQZ4D","created_at":"2026-06-01T01:03:39.183382+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA","json":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA.json","graph_json":"https://pith.science/api/pith-number/RNYUQZ4DBIKLSM7I3KVRMFA3MA/graph.json","events_json":"https://pith.science/api/pith-number/RNYUQZ4DBIKLSM7I3KVRMFA3MA/events.json","paper":"https://pith.science/paper/RNYUQZ4D"},"agent_actions":{"view_html":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA","download_json":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA.json","view_paper":"https://pith.science/paper/RNYUQZ4D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.22967&json=true","fetch_graph":"https://pith.science/api/pith-number/RNYUQZ4DBIKLSM7I3KVRMFA3MA/graph.json","fetch_events":"https://pith.science/api/pith-number/RNYUQZ4DBIKLSM7I3KVRMFA3MA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA/action/storage_attestation","attest_author":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA/action/author_attestation","sign_citation":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA/action/citation_signature","submit_replication":"https://pith.science/pith/RNYUQZ4DBIKLSM7I3KVRMFA3MA/action/replication_record"}},"created_at":"2026-06-01T01:03:39.183382+00:00","updated_at":"2026-06-01T01:03:39.183382+00:00"}