{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WJATLNKM27I5O2P45WNHHIH2TG","short_pith_number":"pith:WJATLNKM","schema_version":"1.0","canonical_sha256":"b24135b54cd7d1d769fced9a73a0fa999f07719c1536d4b0efe8d1b7f5a32fd1","source":{"kind":"arxiv","id":"2606.09934","version":1},"attestation_state":"computed","paper":{"title":"nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Abu Fuad Ahmad, Istiaque Ahmed","submitted_at":"2026-06-07T19:01:05Z","abstract_excerpt":"Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks. Traditional filter methods rank features using global statistics computed symmetrically across classes and thus fail to capture the asymmetry of intrusion detection, where attacks are best characterized as deviations from dominant benign traffic.\n  We propose benign-anchored Classwise Mean Deviation (nCMD), a lightweight and interpretable method that scores feature relevance based on the deviation of attack-class"},"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":"2606.09934","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-07T19:01:05Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"3ff326ac5a95f5d0f271dfe09f5c2b8ba61a1d875319890f9db32e840c5bb682","abstract_canon_sha256":"ab780241fda6cd3387600f9a0be8d6cd090a0c33907cdd9a308ea0049f3f0545"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T00:08:33.708137Z","signature_b64":"su9HIPsf/YO9Oc+DKHRUDbUxkn3CP7WcHn6jcct8XlSyceLHXBOWczXwbNSgfPAWYvMACC1iGHxWBFV8FG18BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b24135b54cd7d1d769fced9a73a0fa999f07719c1536d4b0efe8d1b7f5a32fd1","last_reissued_at":"2026-06-10T00:08:33.707269Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T00:08:33.707269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Abu Fuad Ahmad, Istiaque Ahmed","submitted_at":"2026-06-07T19:01:05Z","abstract_excerpt":"Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks. Traditional filter methods rank features using global statistics computed symmetrically across classes and thus fail to capture the asymmetry of intrusion detection, where attacks are best characterized as deviations from dominant benign traffic.\n  We propose benign-anchored Classwise Mean Deviation (nCMD), a lightweight and interpretable method that scores feature relevance based on the deviation of attack-class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.09934","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/2606.09934/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":"2606.09934","created_at":"2026-06-10T00:08:33.707437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.09934v1","created_at":"2026-06-10T00:08:33.707437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.09934","created_at":"2026-06-10T00:08:33.707437+00:00"},{"alias_kind":"pith_short_12","alias_value":"WJATLNKM27I5","created_at":"2026-06-10T00:08:33.707437+00:00"},{"alias_kind":"pith_short_16","alias_value":"WJATLNKM27I5O2P4","created_at":"2026-06-10T00:08:33.707437+00:00"},{"alias_kind":"pith_short_8","alias_value":"WJATLNKM","created_at":"2026-06-10T00:08:33.707437+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/WJATLNKM27I5O2P45WNHHIH2TG","json":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG.json","graph_json":"https://pith.science/api/pith-number/WJATLNKM27I5O2P45WNHHIH2TG/graph.json","events_json":"https://pith.science/api/pith-number/WJATLNKM27I5O2P45WNHHIH2TG/events.json","paper":"https://pith.science/paper/WJATLNKM"},"agent_actions":{"view_html":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG","download_json":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG.json","view_paper":"https://pith.science/paper/WJATLNKM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.09934&json=true","fetch_graph":"https://pith.science/api/pith-number/WJATLNKM27I5O2P45WNHHIH2TG/graph.json","fetch_events":"https://pith.science/api/pith-number/WJATLNKM27I5O2P45WNHHIH2TG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG/action/storage_attestation","attest_author":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG/action/author_attestation","sign_citation":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG/action/citation_signature","submit_replication":"https://pith.science/pith/WJATLNKM27I5O2P45WNHHIH2TG/action/replication_record"}},"created_at":"2026-06-10T00:08:33.707437+00:00","updated_at":"2026-06-10T00:08:33.707437+00:00"}