{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:M6LT7NGXAFELEZBALVVBFI2CK6","short_pith_number":"pith:M6LT7NGX","schema_version":"1.0","canonical_sha256":"67973fb4d70148b264205d6a12a34257aeadf5debd3fcdcd7400d4eef1904ed4","source":{"kind":"arxiv","id":"2207.08478","version":1},"attestation_state":"computed","paper":{"title":"Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CR","authors_text":"Alexander Pfohl, Clemens Sauerwein","submitted_at":"2022-07-18T09:59:21Z","abstract_excerpt":"The increasingly sophisticated and growing number of threat actors along with the sheer speed at which cyber attacks unfold, make timely identification of attacks imperative to an organisations' security. Consequently, persons responsible for security employ a large variety of information sources concerning emerging attacks, attackers' course of actions or indicators of compromise. However, a vast amount of the needed security information is available in unstructured textual form, which complicates the automated and timely extraction of attackers' Tactics, Techniques and Procedures (TTPs). In "},"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":"2207.08478","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-07-18T09:59:21Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"5af67a4ccd90be5425f02a37a1e15e22ef02e3e50b9bdec4f89584b8e4d6f180","abstract_canon_sha256":"5d894b1047976a9c821e0df7c231a6b0e69aac81b57a44442e109ecd89175bbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:41:05.054297Z","signature_b64":"nFN8sfwCkO1f+i+wdKhIIydzTueXck0w4SgGy8msrcunPTO2mrLa/PAs8m55+TV0ut49+LxsQoN6/1iZx3UQBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67973fb4d70148b264205d6a12a34257aeadf5debd3fcdcd7400d4eef1904ed4","last_reissued_at":"2026-07-05T04:41:05.053932Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:41:05.053932Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CR","authors_text":"Alexander Pfohl, Clemens Sauerwein","submitted_at":"2022-07-18T09:59:21Z","abstract_excerpt":"The increasingly sophisticated and growing number of threat actors along with the sheer speed at which cyber attacks unfold, make timely identification of attacks imperative to an organisations' security. Consequently, persons responsible for security employ a large variety of information sources concerning emerging attacks, attackers' course of actions or indicators of compromise. However, a vast amount of the needed security information is available in unstructured textual form, which complicates the automated and timely extraction of attackers' Tactics, Techniques and Procedures (TTPs). In "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.08478","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/2207.08478/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":"2207.08478","created_at":"2026-07-05T04:41:05.053984+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.08478v1","created_at":"2026-07-05T04:41:05.053984+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.08478","created_at":"2026-07-05T04:41:05.053984+00:00"},{"alias_kind":"pith_short_12","alias_value":"M6LT7NGXAFEL","created_at":"2026-07-05T04:41:05.053984+00:00"},{"alias_kind":"pith_short_16","alias_value":"M6LT7NGXAFELEZBA","created_at":"2026-07-05T04:41:05.053984+00:00"},{"alias_kind":"pith_short_8","alias_value":"M6LT7NGX","created_at":"2026-07-05T04:41:05.053984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18755","citing_title":"Cyber-Attack Technique Classification Using Two-Stage Trained Large Language Models","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6","json":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6.json","graph_json":"https://pith.science/api/pith-number/M6LT7NGXAFELEZBALVVBFI2CK6/graph.json","events_json":"https://pith.science/api/pith-number/M6LT7NGXAFELEZBALVVBFI2CK6/events.json","paper":"https://pith.science/paper/M6LT7NGX"},"agent_actions":{"view_html":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6","download_json":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6.json","view_paper":"https://pith.science/paper/M6LT7NGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.08478&json=true","fetch_graph":"https://pith.science/api/pith-number/M6LT7NGXAFELEZBALVVBFI2CK6/graph.json","fetch_events":"https://pith.science/api/pith-number/M6LT7NGXAFELEZBALVVBFI2CK6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6/action/storage_attestation","attest_author":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6/action/author_attestation","sign_citation":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6/action/citation_signature","submit_replication":"https://pith.science/pith/M6LT7NGXAFELEZBALVVBFI2CK6/action/replication_record"}},"created_at":"2026-07-05T04:41:05.053984+00:00","updated_at":"2026-07-05T04:41:05.053984+00:00"}