{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:CAHJZOULIQFYNCXPQLTPQGVPCU","short_pith_number":"pith:CAHJZOUL","schema_version":"1.0","canonical_sha256":"100e9cba8b440b868aef82e6f81aaf15379eb139d5a595201367b980ee64723d","source":{"kind":"arxiv","id":"2010.13637","version":2},"attestation_state":"computed","paper":{"title":"Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.DB"],"primary_cat":"cs.CR","authors_text":"Dawn Song, Fei Shao, Fengyuan Xu, Peng Gao, Prateek Mittal, Sanjeev R. Kulkarni, Xiaoyuan Liu, Xusheng Xiao, Zheng Qin","submitted_at":"2020-10-26T14:54:01Z","abstract_excerpt":"Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we propose ThreatRaptor, a system that facilitates threat hunting in computer systems using OSCTI. Built upon system auditing frameworks, ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructure"},"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":"2010.13637","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2020-10-26T14:54:01Z","cross_cats_sorted":["cs.CL","cs.DB"],"title_canon_sha256":"e5a61e77880a2f70ee830c1c4c6b5e78acb152c1da955e7f97204833645f0215","abstract_canon_sha256":"d59848a3d5a774b2602b791b68bfe6a3920899e3fa5715e360d9aef3d44fddcf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:18:18.551097Z","signature_b64":"o9XisCKjqnsfm5AWvbyneUVocM9ZRZ1PVOohXjrCmyGUG0uOpH83P8QC9J7eD1d+982gYajJph8+4NvW1i4gBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"100e9cba8b440b868aef82e6f81aaf15379eb139d5a595201367b980ee64723d","last_reissued_at":"2026-07-05T02:18:18.550715Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:18:18.550715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.DB"],"primary_cat":"cs.CR","authors_text":"Dawn Song, Fei Shao, Fengyuan Xu, Peng Gao, Prateek Mittal, Sanjeev R. Kulkarni, Xiaoyuan Liu, Xusheng Xiao, Zheng Qin","submitted_at":"2020-10-26T14:54:01Z","abstract_excerpt":"Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we propose ThreatRaptor, a system that facilitates threat hunting in computer systems using OSCTI. Built upon system auditing frameworks, ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructure"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.13637","kind":"arxiv","version":2},"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/2010.13637/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":"2010.13637","created_at":"2026-07-05T02:18:18.550776+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.13637v2","created_at":"2026-07-05T02:18:18.550776+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.13637","created_at":"2026-07-05T02:18:18.550776+00:00"},{"alias_kind":"pith_short_12","alias_value":"CAHJZOULIQFY","created_at":"2026-07-05T02:18:18.550776+00:00"},{"alias_kind":"pith_short_16","alias_value":"CAHJZOULIQFYNCXP","created_at":"2026-07-05T02:18:18.550776+00:00"},{"alias_kind":"pith_short_8","alias_value":"CAHJZOUL","created_at":"2026-07-05T02:18:18.550776+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/CAHJZOULIQFYNCXPQLTPQGVPCU","json":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU.json","graph_json":"https://pith.science/api/pith-number/CAHJZOULIQFYNCXPQLTPQGVPCU/graph.json","events_json":"https://pith.science/api/pith-number/CAHJZOULIQFYNCXPQLTPQGVPCU/events.json","paper":"https://pith.science/paper/CAHJZOUL"},"agent_actions":{"view_html":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU","download_json":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU.json","view_paper":"https://pith.science/paper/CAHJZOUL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.13637&json=true","fetch_graph":"https://pith.science/api/pith-number/CAHJZOULIQFYNCXPQLTPQGVPCU/graph.json","fetch_events":"https://pith.science/api/pith-number/CAHJZOULIQFYNCXPQLTPQGVPCU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU/action/storage_attestation","attest_author":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU/action/author_attestation","sign_citation":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU/action/citation_signature","submit_replication":"https://pith.science/pith/CAHJZOULIQFYNCXPQLTPQGVPCU/action/replication_record"}},"created_at":"2026-07-05T02:18:18.550776+00:00","updated_at":"2026-07-05T02:18:18.550776+00:00"}