{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:I7MP7CJQB7QXRSIXQI6D32L7KG","short_pith_number":"pith:I7MP7CJQ","schema_version":"1.0","canonical_sha256":"47d8ff89300fe178c917823c3de97f5187db7d31999a3f582881b0ef4f996906","source":{"kind":"arxiv","id":"2508.11472","version":1},"attestation_state":"computed","paper":{"title":"RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-sphere Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Sihan Xu, Xiangrui Cai, Xiaojie Yuan, Xinyu Jiao, Yang Wang, Yaxin Zhao, Ying Zhang","submitted_at":"2025-08-15T13:36:03Z","abstract_excerpt":"Insider threat detection aims to identify malicious user behavior by analyzing logs that record user interactions. Due to the lack of fine-grained behavior-level annotations, detecting specific behavior-level anomalies within user behavior sequences is challenging. Unsupervised methods face high false positive rates and miss rates due to the inherent ambiguity between normal and anomalous behaviors. In this work, we instead introduce weak labels of behavior sequences, which have lower annotation costs, i.e., the training labels (anomalous or normal) are at sequence-level instead of behavior-le"},"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":"2508.11472","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-08-15T13:36:03Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5f014f56a76910bbbf68cbcae48987381876bb79ef0c4d59130c28fa6c975465","abstract_canon_sha256":"9c2fb256dfc81d2aaaa18d3b102a0670131a58fb3fece3812ae148db6330b19e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:33.970310Z","signature_b64":"pG/85mL7i6ANB453OmXviPUv9GsdAzpLd0EwxSITqZnID2HKAEK0uafbq1sN1whWX3A5VX2OPgJ7dia7Jqp3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47d8ff89300fe178c917823c3de97f5187db7d31999a3f582881b0ef4f996906","last_reissued_at":"2026-07-05T11:54:33.969932Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:33.969932Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-sphere Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Sihan Xu, Xiangrui Cai, Xiaojie Yuan, Xinyu Jiao, Yang Wang, Yaxin Zhao, Ying Zhang","submitted_at":"2025-08-15T13:36:03Z","abstract_excerpt":"Insider threat detection aims to identify malicious user behavior by analyzing logs that record user interactions. Due to the lack of fine-grained behavior-level annotations, detecting specific behavior-level anomalies within user behavior sequences is challenging. Unsupervised methods face high false positive rates and miss rates due to the inherent ambiguity between normal and anomalous behaviors. In this work, we instead introduce weak labels of behavior sequences, which have lower annotation costs, i.e., the training labels (anomalous or normal) are at sequence-level instead of behavior-le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.11472","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/2508.11472/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":"2508.11472","created_at":"2026-07-05T11:54:33.969986+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.11472v1","created_at":"2026-07-05T11:54:33.969986+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.11472","created_at":"2026-07-05T11:54:33.969986+00:00"},{"alias_kind":"pith_short_12","alias_value":"I7MP7CJQB7QX","created_at":"2026-07-05T11:54:33.969986+00:00"},{"alias_kind":"pith_short_16","alias_value":"I7MP7CJQB7QXRSIX","created_at":"2026-07-05T11:54:33.969986+00:00"},{"alias_kind":"pith_short_8","alias_value":"I7MP7CJQ","created_at":"2026-07-05T11:54:33.969986+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/I7MP7CJQB7QXRSIXQI6D32L7KG","json":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG.json","graph_json":"https://pith.science/api/pith-number/I7MP7CJQB7QXRSIXQI6D32L7KG/graph.json","events_json":"https://pith.science/api/pith-number/I7MP7CJQB7QXRSIXQI6D32L7KG/events.json","paper":"https://pith.science/paper/I7MP7CJQ"},"agent_actions":{"view_html":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG","download_json":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG.json","view_paper":"https://pith.science/paper/I7MP7CJQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.11472&json=true","fetch_graph":"https://pith.science/api/pith-number/I7MP7CJQB7QXRSIXQI6D32L7KG/graph.json","fetch_events":"https://pith.science/api/pith-number/I7MP7CJQB7QXRSIXQI6D32L7KG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG/action/storage_attestation","attest_author":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG/action/author_attestation","sign_citation":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG/action/citation_signature","submit_replication":"https://pith.science/pith/I7MP7CJQB7QXRSIXQI6D32L7KG/action/replication_record"}},"created_at":"2026-07-05T11:54:33.969986+00:00","updated_at":"2026-07-05T11:54:33.969986+00:00"}