{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:K7EW3WHVR4DXIZZJGTTRA7S627","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a78518d913c877b764de0959b1d0e944f9c41aaae68b46eafb2640971da7bb56","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-04T17:20:01Z","title_canon_sha256":"efc5146b588efc4a2366b5ffd468088acb73b3681d29a642eed6f99bcfbb1dea"},"schema_version":"1.0","source":{"id":"2412.03483","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.03483","created_at":"2026-07-05T11:03:18Z"},{"alias_kind":"arxiv_version","alias_value":"2412.03483v2","created_at":"2026-07-05T11:03:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.03483","created_at":"2026-07-05T11:03:18Z"},{"alias_kind":"pith_short_12","alias_value":"K7EW3WHVR4DX","created_at":"2026-07-05T11:03:18Z"},{"alias_kind":"pith_short_16","alias_value":"K7EW3WHVR4DXIZZJ","created_at":"2026-07-05T11:03:18Z"},{"alias_kind":"pith_short_8","alias_value":"K7EW3WHV","created_at":"2026-07-05T11:03:18Z"}],"graph_snapshots":[{"event_id":"sha256:fca962837349735126c451c8a56bb3f1a23897e039dae5b3398697f226a4406d","target":"graph","created_at":"2026-07-05T11:03:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.03483/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new security threats. Therefore, 6G/NextG networks must be equipped with advanced Artificial Intelligence algorithms, in order to evade these attacks. Existing studies on the intrusion detection task rely on the train of shallow machine learning classifiers, including Logistic Regression, Decision Trees, and so on, yielding suboptimal performance. Others are based on deep neural networks consisting of static components,","authors_text":"Christos Ntanos, Dimitris Askounis, George Doukas, Loukas Ilias, Vangelis Lamprou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-04T17:20:01Z","title":"Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.03483","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f0183845fcca37da2934831fc5f7c4a11fc1ff765785c5f3edf951fc77cbbfe8","target":"record","created_at":"2026-07-05T11:03:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a78518d913c877b764de0959b1d0e944f9c41aaae68b46eafb2640971da7bb56","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-04T17:20:01Z","title_canon_sha256":"efc5146b588efc4a2366b5ffd468088acb73b3681d29a642eed6f99bcfbb1dea"},"schema_version":"1.0","source":{"id":"2412.03483","kind":"arxiv","version":2}},"canonical_sha256":"57c96dd8f58f0774672934e7107e5ed7d8d1f04dc900370f01d453e6ef173c16","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"57c96dd8f58f0774672934e7107e5ed7d8d1f04dc900370f01d453e6ef173c16","first_computed_at":"2026-07-05T11:03:18.963298Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:03:18.963298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s7FEkZdsszSN/k8c472FUXwVx2PLBAncLYgZVY3gXPqRRubrJ2cR6FyICSg+Ny1U0ZXVETmSBnr3c8dIoCqsAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:03:18.963807Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.03483","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0183845fcca37da2934831fc5f7c4a11fc1ff765785c5f3edf951fc77cbbfe8","sha256:fca962837349735126c451c8a56bb3f1a23897e039dae5b3398697f226a4406d"],"state_sha256":"e76e3a99ffd4a0da6b6e4103fa8f87e9855d63165f14c9c71a58e115fee84bc1"}