{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KWFWXPDPWJ2R7EWUBK4E6YBVNA","short_pith_number":"pith:KWFWXPDP","schema_version":"1.0","canonical_sha256":"558b6bbc6fb2751f92d40ab84f6035682f4e63c4d874a74deefd8f0505a5edcb","source":{"kind":"arxiv","id":"2212.03793","version":2},"attestation_state":"computed","paper":{"title":"RADAR: A TTP-based Extensible, Explainable, and Effective System for Network Traffic Analysis and Malware Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ivan Martinovic, Simon Birnbach, Yashovardhan Sharma","submitted_at":"2022-12-07T17:19:43Z","abstract_excerpt":"Network analysis and machine learning techniques have been widely applied for building malware detection systems. Though these systems attain impressive results, they often are $(i)$ not extensible, being monolithic, well tuned for the specific task they have been designed for but very difficult to adapt and/or extend to other settings, and $(ii)$ not interpretable, being black boxes whose inner complexity makes it impossible to link the result of detection with its root cause, making further analysis of threats a challenge. In this paper we present RADAR, an extensible and explainable system "},"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":"2212.03793","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2022-12-07T17:19:43Z","cross_cats_sorted":[],"title_canon_sha256":"c3449a371c730e457d9c2126b0522a1b69d09ee8147e5bf6c15b0f50acaba05d","abstract_canon_sha256":"243b1de1331c414aa06d95f3ba761623cf695cf6c34d2f0841a5cbcbcac20d10"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:00:35.319437Z","signature_b64":"ywucqcKMp3z24S4rspQzvZJBomabe6IIgSnTcAY8i3bKLnrkYDQfZ8MmXhGN2CF2byF22tImpwWd9joZB2AcCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"558b6bbc6fb2751f92d40ab84f6035682f4e63c4d874a74deefd8f0505a5edcb","last_reissued_at":"2026-07-05T06:00:35.318950Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:00:35.318950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RADAR: A TTP-based Extensible, Explainable, and Effective System for Network Traffic Analysis and Malware Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ivan Martinovic, Simon Birnbach, Yashovardhan Sharma","submitted_at":"2022-12-07T17:19:43Z","abstract_excerpt":"Network analysis and machine learning techniques have been widely applied for building malware detection systems. Though these systems attain impressive results, they often are $(i)$ not extensible, being monolithic, well tuned for the specific task they have been designed for but very difficult to adapt and/or extend to other settings, and $(ii)$ not interpretable, being black boxes whose inner complexity makes it impossible to link the result of detection with its root cause, making further analysis of threats a challenge. In this paper we present RADAR, an extensible and explainable system "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.03793","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/2212.03793/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":"2212.03793","created_at":"2026-07-05T06:00:35.319010+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.03793v2","created_at":"2026-07-05T06:00:35.319010+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.03793","created_at":"2026-07-05T06:00:35.319010+00:00"},{"alias_kind":"pith_short_12","alias_value":"KWFWXPDPWJ2R","created_at":"2026-07-05T06:00:35.319010+00:00"},{"alias_kind":"pith_short_16","alias_value":"KWFWXPDPWJ2R7EWU","created_at":"2026-07-05T06:00:35.319010+00:00"},{"alias_kind":"pith_short_8","alias_value":"KWFWXPDP","created_at":"2026-07-05T06:00:35.319010+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.10978","citing_title":"Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA","json":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA.json","graph_json":"https://pith.science/api/pith-number/KWFWXPDPWJ2R7EWUBK4E6YBVNA/graph.json","events_json":"https://pith.science/api/pith-number/KWFWXPDPWJ2R7EWUBK4E6YBVNA/events.json","paper":"https://pith.science/paper/KWFWXPDP"},"agent_actions":{"view_html":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA","download_json":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA.json","view_paper":"https://pith.science/paper/KWFWXPDP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.03793&json=true","fetch_graph":"https://pith.science/api/pith-number/KWFWXPDPWJ2R7EWUBK4E6YBVNA/graph.json","fetch_events":"https://pith.science/api/pith-number/KWFWXPDPWJ2R7EWUBK4E6YBVNA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA/action/storage_attestation","attest_author":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA/action/author_attestation","sign_citation":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA/action/citation_signature","submit_replication":"https://pith.science/pith/KWFWXPDPWJ2R7EWUBK4E6YBVNA/action/replication_record"}},"created_at":"2026-07-05T06:00:35.319010+00:00","updated_at":"2026-07-05T06:00:35.319010+00:00"}