{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:FYUYNEQKBL6UPWZGQUKURZBC73","short_pith_number":"pith:FYUYNEQK","canonical_record":{"source":{"id":"2606.19220","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-17T15:57:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7c5ac6fc85026ffa9e93bcaba7cafa7e50afec010545357178eccf3301be4ed2","abstract_canon_sha256":"36c320f6c12fcd320ef1057c77e2f5f1e6688adc268eae9d8e147df44bcc265c"},"schema_version":"1.0"},"canonical_sha256":"2e2986920a0afd47db26851548e422fef19ddff03e898a23fcce10c74d96f997","source":{"kind":"arxiv","id":"2606.19220","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.19220","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"arxiv_version","alias_value":"2606.19220v1","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19220","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"pith_short_12","alias_value":"FYUYNEQKBL6U","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"pith_short_16","alias_value":"FYUYNEQKBL6UPWZG","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"pith_short_8","alias_value":"FYUYNEQK","created_at":"2026-06-19T16:12:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:FYUYNEQKBL6UPWZGQUKURZBC73","target":"record","payload":{"canonical_record":{"source":{"id":"2606.19220","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-17T15:57:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7c5ac6fc85026ffa9e93bcaba7cafa7e50afec010545357178eccf3301be4ed2","abstract_canon_sha256":"36c320f6c12fcd320ef1057c77e2f5f1e6688adc268eae9d8e147df44bcc265c"},"schema_version":"1.0"},"canonical_sha256":"2e2986920a0afd47db26851548e422fef19ddff03e898a23fcce10c74d96f997","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:08.120474Z","signature_b64":"6RyGNqhwDr7ryD6DcS6VTj97W+nk1TbnYX9zraB+a6axeMHt2Jc7rimAeCLPtYqWfzngg9In5eMJ/hNQhuZ1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e2986920a0afd47db26851548e422fef19ddff03e898a23fcce10c74d96f997","last_reissued_at":"2026-06-19T16:12:08.120124Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:08.120124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.19220","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-19T16:12:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"//xx6kTMMrWBYFYyhsn40SlcZZTtV4oNtBySanYeNpDj0jq7Og1jQhIV9h2rGWDbii7aZUEdMie8/FMw7FBuDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T07:09:22.540470Z"},"content_sha256":"8de9b815e180f71758543a9dc4f74cd1e2dd56f6e17a4d724419e2008a7726f3","schema_version":"1.0","event_id":"sha256:8de9b815e180f71758543a9dc4f74cd1e2dd56f6e17a4d724419e2008a7726f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:FYUYNEQKBL6UPWZGQUKURZBC73","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Unlearning for the XGBoost Model with Network Intrusion Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Diana Magalh\\~aes, Eva Maia, Isabel Pra\\c{c}a, Jo\\~ao Vitorino","submitted_at":"2026-06-17T15:57:43Z","abstract_excerpt":"Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU research focuses on deep learning and image data, leaving a gap in the domain of network intrusion detection, which relies heavily on tabular data. This work introduces XGBoost-Forget, an unlearning approach for the XGBoost model, to address this gap. The approach is evaluated on two tabular Network Intrusion (NI) datasets, IoT-23 and GeNIS, using multiple metrics to assess model performance, unlearning efficiency, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19220","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/2606.19220/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-19T16:12:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QPzZS7hq+l08gnMUoIgz8tQBVk0KsB1mJws+EHME7/aBa5N95Gf5rxyTbAf1VH0mdLKgAmn8KSsQEBjFgtk5Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T07:09:22.540849Z"},"content_sha256":"a1fa0500263374f362bde9b45bc734f8ad78a52afc1e5bb3c00a02016c952e13","schema_version":"1.0","event_id":"sha256:a1fa0500263374f362bde9b45bc734f8ad78a52afc1e5bb3c00a02016c952e13"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FYUYNEQKBL6UPWZGQUKURZBC73/bundle.json","state_url":"https://pith.science/pith/FYUYNEQKBL6UPWZGQUKURZBC73/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FYUYNEQKBL6UPWZGQUKURZBC73/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-21T07:09:22Z","links":{"resolver":"https://pith.science/pith/FYUYNEQKBL6UPWZGQUKURZBC73","bundle":"https://pith.science/pith/FYUYNEQKBL6UPWZGQUKURZBC73/bundle.json","state":"https://pith.science/pith/FYUYNEQKBL6UPWZGQUKURZBC73/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FYUYNEQKBL6UPWZGQUKURZBC73/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:FYUYNEQKBL6UPWZGQUKURZBC73","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":"36c320f6c12fcd320ef1057c77e2f5f1e6688adc268eae9d8e147df44bcc265c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-17T15:57:43Z","title_canon_sha256":"7c5ac6fc85026ffa9e93bcaba7cafa7e50afec010545357178eccf3301be4ed2"},"schema_version":"1.0","source":{"id":"2606.19220","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.19220","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"arxiv_version","alias_value":"2606.19220v1","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19220","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"pith_short_12","alias_value":"FYUYNEQKBL6U","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"pith_short_16","alias_value":"FYUYNEQKBL6UPWZG","created_at":"2026-06-19T16:12:08Z"},{"alias_kind":"pith_short_8","alias_value":"FYUYNEQK","created_at":"2026-06-19T16:12:08Z"}],"graph_snapshots":[{"event_id":"sha256:a1fa0500263374f362bde9b45bc734f8ad78a52afc1e5bb3c00a02016c952e13","target":"graph","created_at":"2026-06-19T16:12:08Z","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/2606.19220/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU research focuses on deep learning and image data, leaving a gap in the domain of network intrusion detection, which relies heavily on tabular data. This work introduces XGBoost-Forget, an unlearning approach for the XGBoost model, to address this gap. The approach is evaluated on two tabular Network Intrusion (NI) datasets, IoT-23 and GeNIS, using multiple metrics to assess model performance, unlearning efficiency, and ","authors_text":"Diana Magalh\\~aes, Eva Maia, Isabel Pra\\c{c}a, Jo\\~ao Vitorino","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-17T15:57:43Z","title":"Machine Unlearning for the XGBoost Model with Network Intrusion Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19220","kind":"arxiv","version":1},"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":"sha256:8de9b815e180f71758543a9dc4f74cd1e2dd56f6e17a4d724419e2008a7726f3","target":"record","created_at":"2026-06-19T16:12:08Z","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":"36c320f6c12fcd320ef1057c77e2f5f1e6688adc268eae9d8e147df44bcc265c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-17T15:57:43Z","title_canon_sha256":"7c5ac6fc85026ffa9e93bcaba7cafa7e50afec010545357178eccf3301be4ed2"},"schema_version":"1.0","source":{"id":"2606.19220","kind":"arxiv","version":1}},"canonical_sha256":"2e2986920a0afd47db26851548e422fef19ddff03e898a23fcce10c74d96f997","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e2986920a0afd47db26851548e422fef19ddff03e898a23fcce10c74d96f997","first_computed_at":"2026-06-19T16:12:08.120124Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:12:08.120124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6RyGNqhwDr7ryD6DcS6VTj97W+nk1TbnYX9zraB+a6axeMHt2Jc7rimAeCLPtYqWfzngg9In5eMJ/hNQhuZ1Aw==","signature_status":"signed_v1","signed_at":"2026-06-19T16:12:08.120474Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.19220","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8de9b815e180f71758543a9dc4f74cd1e2dd56f6e17a4d724419e2008a7726f3","sha256:a1fa0500263374f362bde9b45bc734f8ad78a52afc1e5bb3c00a02016c952e13"],"state_sha256":"9336dca3193f64d951a4d7e8e6c261f874d12affd8af6543679583a5895ee653"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2fl0J6iIBXOEB9QUCpSudK7HyiWKJrYAfa5QuwXHicsdttZsOTzWVm+BZ7X7vOkmPDAawHS/ba3WyBDzAp6zCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T07:09:22.542839Z","bundle_sha256":"1e1376fe735ffd3fe4c3ac9b7a6486a76118bfdfbd896af3571522d34d0fc807"}}