{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:K3NDZWHJAU33GK4QILCXT23S54","short_pith_number":"pith:K3NDZWHJ","schema_version":"1.0","canonical_sha256":"56da3cd8e90537b32b9042c579eb72ef37933437cf9b5dda9081b310756f36b5","source":{"kind":"arxiv","id":"2606.11565","version":1},"attestation_state":"computed","paper":{"title":"A Deterministic Forensic Preprocessing Framework for Heterogeneous Network Datasets: Formal Foundations, Implementation, and Empirical Validation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Nasim Ferdosian, Nickson M. Karie, Qian Li, Ravi Chaudhary, Reza Ryan","submitted_at":"2026-06-10T01:45:09Z","abstract_excerpt":"Digital forensic investigations increasingly depend on preprocessing heterogeneous network evidence from intrusion detection systems, IoT devices, and enterprise traffic logs. Incompatible schemas and timestamp formats hinder evidence correlation and timeline reconstruction, while current ad hoc approaches offer no mechanism to verify consistency across runs or analysis, creating reproducibility gaps that challenge evidence admissibility. This paper introduces a deterministic forensic preprocessing framework that converts heterogeneous network datasets into a reproducible canonical form. The f"},"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":"2606.11565","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-06-10T01:45:09Z","cross_cats_sorted":[],"title_canon_sha256":"36eb29a8c43e9290b358d1f2d508262e32de09eb7a0524dac790c6a8c886766f","abstract_canon_sha256":"863f067d9e00d973abdfec3440058024ef88909a6f19d2df679bafb0730119f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:56.384070Z","signature_b64":"K+PThUKw64QQPIq4q6443eoGmGC71qVV07GQftFSINZB4IZxEjg5Oa00drCdKH0Gqmz1cBtRg8f7be56askbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56da3cd8e90537b32b9042c579eb72ef37933437cf9b5dda9081b310756f36b5","last_reissued_at":"2026-06-11T01:09:56.383266Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:56.383266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deterministic Forensic Preprocessing Framework for Heterogeneous Network Datasets: Formal Foundations, Implementation, and Empirical Validation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Nasim Ferdosian, Nickson M. Karie, Qian Li, Ravi Chaudhary, Reza Ryan","submitted_at":"2026-06-10T01:45:09Z","abstract_excerpt":"Digital forensic investigations increasingly depend on preprocessing heterogeneous network evidence from intrusion detection systems, IoT devices, and enterprise traffic logs. Incompatible schemas and timestamp formats hinder evidence correlation and timeline reconstruction, while current ad hoc approaches offer no mechanism to verify consistency across runs or analysis, creating reproducibility gaps that challenge evidence admissibility. This paper introduces a deterministic forensic preprocessing framework that converts heterogeneous network datasets into a reproducible canonical form. The f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11565","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/2606.11565/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":"2606.11565","created_at":"2026-06-11T01:09:56.383409+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11565v1","created_at":"2026-06-11T01:09:56.383409+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11565","created_at":"2026-06-11T01:09:56.383409+00:00"},{"alias_kind":"pith_short_12","alias_value":"K3NDZWHJAU33","created_at":"2026-06-11T01:09:56.383409+00:00"},{"alias_kind":"pith_short_16","alias_value":"K3NDZWHJAU33GK4Q","created_at":"2026-06-11T01:09:56.383409+00:00"},{"alias_kind":"pith_short_8","alias_value":"K3NDZWHJ","created_at":"2026-06-11T01:09:56.383409+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/K3NDZWHJAU33GK4QILCXT23S54","json":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54.json","graph_json":"https://pith.science/api/pith-number/K3NDZWHJAU33GK4QILCXT23S54/graph.json","events_json":"https://pith.science/api/pith-number/K3NDZWHJAU33GK4QILCXT23S54/events.json","paper":"https://pith.science/paper/K3NDZWHJ"},"agent_actions":{"view_html":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54","download_json":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54.json","view_paper":"https://pith.science/paper/K3NDZWHJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11565&json=true","fetch_graph":"https://pith.science/api/pith-number/K3NDZWHJAU33GK4QILCXT23S54/graph.json","fetch_events":"https://pith.science/api/pith-number/K3NDZWHJAU33GK4QILCXT23S54/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54/action/storage_attestation","attest_author":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54/action/author_attestation","sign_citation":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54/action/citation_signature","submit_replication":"https://pith.science/pith/K3NDZWHJAU33GK4QILCXT23S54/action/replication_record"}},"created_at":"2026-06-11T01:09:56.383409+00:00","updated_at":"2026-06-11T01:09:56.383409+00:00"}