{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MCBJGA6GR3ZKMK5GOSK2OJ4DRY","short_pith_number":"pith:MCBJGA6G","schema_version":"1.0","canonical_sha256":"60829303c68ef2a62ba67495a727838e274f61ba554d49b46df5a630d4bf992a","source":{"kind":"arxiv","id":"1907.10272","version":1},"attestation_state":"computed","paper":{"title":"Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Adam James Hall, Naghmeh Moradpoor, Nikolaos Pitropakis, William J Buchanan","submitted_at":"2019-07-24T07:16:33Z","abstract_excerpt":"Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exists between the requirements of this community and the solutions that are currently available. This paper uses the CERT dataset r4.2 along with a series of machine learning classifiers to predict the occurrence of a particular malicious insider threat scenario - the uploading sensitive information to wiki leaks before leaving the organization. These algorithms are aggregated into a meta-classifier which has a stronger pred"},"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":"1907.10272","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2019-07-24T07:16:33Z","cross_cats_sorted":[],"title_canon_sha256":"dcbd2758a5bcf6f3805948feeab1b3acebe54a3281b90b32410a7382f36ef459","abstract_canon_sha256":"63159a280dd368351c856ad62191655a55b1b45429c54d63d23f767c318eb202"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:38.426881Z","signature_b64":"Q8SHd6524lN9vdCVotcc8yLUfo+YLklIjRALhuVH18Ac8SX3CBbUpZlxMPEjvDm2yFImE1HdN0FoheZ2XPDZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60829303c68ef2a62ba67495a727838e274f61ba554d49b46df5a630d4bf992a","last_reissued_at":"2026-05-17T23:39:38.426214Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:38.426214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Adam James Hall, Naghmeh Moradpoor, Nikolaos Pitropakis, William J Buchanan","submitted_at":"2019-07-24T07:16:33Z","abstract_excerpt":"Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exists between the requirements of this community and the solutions that are currently available. This paper uses the CERT dataset r4.2 along with a series of machine learning classifiers to predict the occurrence of a particular malicious insider threat scenario - the uploading sensitive information to wiki leaks before leaving the organization. These algorithms are aggregated into a meta-classifier which has a stronger pred"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10272","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":""},"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":"1907.10272","created_at":"2026-05-17T23:39:38.426332+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.10272v1","created_at":"2026-05-17T23:39:38.426332+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10272","created_at":"2026-05-17T23:39:38.426332+00:00"},{"alias_kind":"pith_short_12","alias_value":"MCBJGA6GR3ZK","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"MCBJGA6GR3ZKMK5G","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"MCBJGA6G","created_at":"2026-05-18T12:33:21.387695+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/MCBJGA6GR3ZKMK5GOSK2OJ4DRY","json":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY.json","graph_json":"https://pith.science/api/pith-number/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/graph.json","events_json":"https://pith.science/api/pith-number/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/events.json","paper":"https://pith.science/paper/MCBJGA6G"},"agent_actions":{"view_html":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY","download_json":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY.json","view_paper":"https://pith.science/paper/MCBJGA6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.10272&json=true","fetch_graph":"https://pith.science/api/pith-number/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/graph.json","fetch_events":"https://pith.science/api/pith-number/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/action/storage_attestation","attest_author":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/action/author_attestation","sign_citation":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/action/citation_signature","submit_replication":"https://pith.science/pith/MCBJGA6GR3ZKMK5GOSK2OJ4DRY/action/replication_record"}},"created_at":"2026-05-17T23:39:38.426332+00:00","updated_at":"2026-05-17T23:39:38.426332+00:00"}