{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:IXPCHCOL7KFBXBDFROUMN7MFYD","short_pith_number":"pith:IXPCHCOL","schema_version":"1.0","canonical_sha256":"45de2389cbfa8a1b84658ba8c6fd85c0d714832464931c06f33cca1f36e78b15","source":{"kind":"arxiv","id":"1509.08239","version":1},"attestation_state":"computed","paper":{"title":"Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Biju Issac, Mohanad Albayati","submitted_at":"2015-09-28T09:01:30Z","abstract_excerpt":"In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we devel"},"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":"1509.08239","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2015-09-28T09:01:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b3650355d5ffe1df2f5b12f5dcd98efa0cae48543205d5e94fae3c43dbf4deaf","abstract_canon_sha256":"7c5d65fd032a124b8242a435ab21da5093e4bfae922bf83ecccef419ae5a69e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:31:54.364348Z","signature_b64":"/f2z5StU8e/JLc7Tbt4XUqz+tupSncOi0DP+HUw+0IEVBr4wBLNcFfW5B6GLqpuu1fb544S2uD26a9i2HrRiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45de2389cbfa8a1b84658ba8c6fd85c0d714832464931c06f33cca1f36e78b15","last_reissued_at":"2026-05-18T01:31:54.363904Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:31:54.363904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Biju Issac, Mohanad Albayati","submitted_at":"2015-09-28T09:01:30Z","abstract_excerpt":"In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we devel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.08239","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":"1509.08239","created_at":"2026-05-18T01:31:54.363972+00:00"},{"alias_kind":"arxiv_version","alias_value":"1509.08239v1","created_at":"2026-05-18T01:31:54.363972+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.08239","created_at":"2026-05-18T01:31:54.363972+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXPCHCOL7KFB","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXPCHCOL7KFBXBDF","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXPCHCOL","created_at":"2026-05-18T12:29:27.538025+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/IXPCHCOL7KFBXBDFROUMN7MFYD","json":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD.json","graph_json":"https://pith.science/api/pith-number/IXPCHCOL7KFBXBDFROUMN7MFYD/graph.json","events_json":"https://pith.science/api/pith-number/IXPCHCOL7KFBXBDFROUMN7MFYD/events.json","paper":"https://pith.science/paper/IXPCHCOL"},"agent_actions":{"view_html":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD","download_json":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD.json","view_paper":"https://pith.science/paper/IXPCHCOL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1509.08239&json=true","fetch_graph":"https://pith.science/api/pith-number/IXPCHCOL7KFBXBDFROUMN7MFYD/graph.json","fetch_events":"https://pith.science/api/pith-number/IXPCHCOL7KFBXBDFROUMN7MFYD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD/action/storage_attestation","attest_author":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD/action/author_attestation","sign_citation":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD/action/citation_signature","submit_replication":"https://pith.science/pith/IXPCHCOL7KFBXBDFROUMN7MFYD/action/replication_record"}},"created_at":"2026-05-18T01:31:54.363972+00:00","updated_at":"2026-05-18T01:31:54.363972+00:00"}