{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:N44HF7KPJEIM2MPTVCYWM3HSJE","short_pith_number":"pith:N44HF7KP","canonical_record":{"source":{"id":"2605.17325","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T08:32:08Z","cross_cats_sorted":[],"title_canon_sha256":"4e76fa505b63076cf7b429a771ff5a7dec93c907c42a680771919304de084bfc","abstract_canon_sha256":"098f3b078010a1bd442ac3bf75bccd6ce4193e5c715366589372b935f8e1b894"},"schema_version":"1.0"},"canonical_sha256":"6f3872fd4f4910cd31f3a8b1666cf2490ed3b33c30ca30b60223f98f173bdc52","source":{"kind":"arxiv","id":"2605.17325","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17325","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17325v1","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17325","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"pith_short_12","alias_value":"N44HF7KPJEIM","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"pith_short_16","alias_value":"N44HF7KPJEIM2MPT","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"pith_short_8","alias_value":"N44HF7KP","created_at":"2026-05-20T00:03:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:N44HF7KPJEIM2MPTVCYWM3HSJE","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17325","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T08:32:08Z","cross_cats_sorted":[],"title_canon_sha256":"4e76fa505b63076cf7b429a771ff5a7dec93c907c42a680771919304de084bfc","abstract_canon_sha256":"098f3b078010a1bd442ac3bf75bccd6ce4193e5c715366589372b935f8e1b894"},"schema_version":"1.0"},"canonical_sha256":"6f3872fd4f4910cd31f3a8b1666cf2490ed3b33c30ca30b60223f98f173bdc52","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:52.222254Z","signature_b64":"Q4I5sdfAvvwc52dHZya90EPTX7SfKH54J4ZnTVS4pc05PGB3Yyc0B69312ZxDAxDTpNi0TGix6wyiSh6NehIDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f3872fd4f4910cd31f3a8b1666cf2490ed3b33c30ca30b60223f98f173bdc52","last_reissued_at":"2026-05-20T00:03:52.221401Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:52.221401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17325","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":"2026-05-20T00:03:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0lJ2BcPPbM80UtLrFLo5CCo4XdsKoU7DWiVxx0AWnZFvK8HK7oPYusO0e2CeuIqEYQ+wLd6+O9TrEGmrrPwpBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T18:06:30.058831Z"},"content_sha256":"8a7db32b9a3c9ff396f7ef52b79fcfc0f1cc00601a9880940f2f15968225124d","schema_version":"1.0","event_id":"sha256:8a7db32b9a3c9ff396f7ef52b79fcfc0f1cc00601a9880940f2f15968225124d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:N44HF7KPJEIM2MPTVCYWM3HSJE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Stream-Processing and Latency-Gated Response for Cross-Sector Threat Detection and Collaborative Containment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A federated stream-processing framework detects coordinated cross-sector threats and achieves containment in 12-20 seconds despite network partitions.","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Namit Mohale","submitted_at":"2026-05-17T08:32:08Z","abstract_excerpt":"Critical infrastructure defense is fundamentally bottlenecked by the operational reality that preventive controls are frequently bypassed by sophisticated supply-chain compromises and stolen administrative credentials. When prevention fails, defense relies entirely on rapid, post-ingress threat detection and automated response across sovereign sectors. We present a novel, federated, high-throughput stream-processing and correlation framework designed to detect coordinated cross-sector threat campaigns and orchestrate containment at machine speed. By utilizing a stateless Pre-Filtering Dispatch"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"By utilizing a stateless Pre-Filtering Dispatcher Subsystem (PFDS), in-memory lock-sharded state workers, and a 95% statistical watermark heuristic, our system maintains detection momentum during network partitions to evacuate speculative alerts and achieves total end-to-end operational convergence within a realistic 12-20 seconds window.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The 500,000 events per second synthetic workload and prototype implementation in Go accurately represent the challenges, data patterns, and operational conditions of real-world multi-sector threat detection and collaborative containment.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A federated stream-processing system with PFDS, in-memory sharded workers, and statistical watermarking achieves end-to-end cross-sector threat detection and containment in 12-20 seconds on a 500k events/sec prototype workload.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A federated stream-processing framework detects coordinated cross-sector threats and achieves containment in 12-20 seconds despite network partitions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c8c70a366143feaa0e85abf553ee347c096fb2962dba788e32aa4e8ad719241a"},"source":{"id":"2605.17325","kind":"arxiv","version":1},"verdict":{"id":"1197a7a7-9eef-40cf-86bd-7f484cb221f5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T23:44:45.807199Z","strongest_claim":"By utilizing a stateless Pre-Filtering Dispatcher Subsystem (PFDS), in-memory lock-sharded state workers, and a 95% statistical watermark heuristic, our system maintains detection momentum during network partitions to evacuate speculative alerts and achieves total end-to-end operational convergence within a realistic 12-20 seconds window.","one_line_summary":"A federated stream-processing system with PFDS, in-memory sharded workers, and statistical watermarking achieves end-to-end cross-sector threat detection and containment in 12-20 seconds on a 500k events/sec prototype workload.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The 500,000 events per second synthetic workload and prototype implementation in Go accurately represent the challenges, data patterns, and operational conditions of real-world multi-sector threat detection and collaborative containment.","pith_extraction_headline":"A federated stream-processing framework detects coordinated cross-sector threats and achieves containment in 12-20 seconds despite network partitions."},"integrity":{"clean":false,"summary":{"advisory":1,"critical":0,"by_detector":{"doi_compliance":{"total":1,"advisory":1,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2605.17325/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/AC-CESS.2024.3454211) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":3,"audited_at":"2026-05-19T23:52:41.527262Z","detected_doi":"10.1109/AC-CESS.2024.3454211","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null}],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-20T00:01:20.649528Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T23:52:41.527262Z","status":"completed","version":"1.0.0","findings_count":1},{"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.813939Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.746834Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"819fbeabc5ce773e2ba3025ee5ebfdceeb50d8be381dc2954610a382dbfb6cbe"},"references":{"count":12,"sample":[{"doi":"","year":2015,"title":"T. Akidau, R. Bradshaw, C. Chambers, S. Chernyak, R.J. Fernández- Moctezuma, R. Lax et al. ‘‘The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbo","work_id":"20c1af1b-06b4-4a52-ae95-94752dac95db","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/ictc66702.2025.11388140","year":2025,"title":"O. Babayomi and D.-S. Kim. ‘‘Federated Anomaly Detection and Mit- igation for EV Charging Forecasting Under Cyberattacks’’, 2025. /em- phInternational Conference on Information and Communication Tech-","work_id":"e88e7c28-297e-479d-bc0a-a937cb5e33f7","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/ac-","year":2024,"title":"Enhancing Digital Image Forgery Detection Us- ing Transfer Learning","work_id":"686153ec-89bd-4634-8bdd-4f80ae648276","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/ojcs.2025.3618157","year":2025,"title":"K. Thirasak, T. Chuaphanngam, D. Chainarong and S. Fugkeaw, ‘‘TF2ML: Threat Filtering With Two-Stage Machine Learning for Effi- cient Provenance-Aware Threat Detection and Response’’,IEEE Open Journal","work_id":"3623392d-a34e-41fd-8b30-8ec1903e7586","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"M. Barni and F. Bartolini,Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications, CRC Press, 2024","work_id":"5611fe6d-d067-49b6-b8b5-49a266b4d537","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":12,"snapshot_sha256":"2a64f95d16e43416eba05f490e9689cbf6c29301dec174d2e7fe0a1661da60e1","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"008da2e4dba339413ff533f4fd7057d722b05d839fccdbe5d4f196775de6fa16"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"1197a7a7-9eef-40cf-86bd-7f484cb221f5"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W+6xUKq+GEfZRewkiepGkoBsPgEDNoFJnpDZ3oMl/bJzRNOAzMPYepR9Z8RE1L0srdhsj+iQHUPq0/iAFemICg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T18:06:30.059687Z"},"content_sha256":"a405afc74e07680f52aead6547faf7455fc8f958913b89d5937e76eab3680cc5","schema_version":"1.0","event_id":"sha256:a405afc74e07680f52aead6547faf7455fc8f958913b89d5937e76eab3680cc5"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:N44HF7KPJEIM2MPTVCYWM3HSJE","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/AC-CESS.2024.3454211) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"A. Vyas, P .-C. Lin, R.-H. Hwang and M. Tripathi, ‘‘Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Sur- vey’’,IEEE Access, vol. 12, pp. 127018-127050, 2024, doi: 10.1109/AC- CESS.2024.3454211","arxiv_id":"2605.17325","detector":"doi_compliance","evidence":{"ref_index":3,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"A. Vyas, P .-C. Lin, R.-H. Hwang and M. Tripathi, ‘‘Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Sur- vey’’,IEEE Access, vol. 12, pp. 127018-127050, 2024, doi: 10.1109/AC- CESS.2024.3454211","reconstructed_doi":"10.1109/AC-CESS.2024.3454211"},"severity":"advisory","ref_index":3,"audited_at":"2026-05-19T23:52:41.527262Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/AC-CESS.2024.3454211","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"7457329776d0eb4e3c2f95cb923025346890bd8f4575b110722534b568b7bd9a","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":3722,"payload_sha256":"90e6b6fba37394df25021f01e20a82ebbec967a8f6861167289cffdfa0925762","signature_b64":"JKHbdblncoHoqnbswZ3jvNDETMPW5VE+qR5R6hy//eZWWMqduf3YflSw40h3z1PTs4Mzji0tBc4p3uNmdlOXAg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T23:57:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"66nqACZrw0WVQu9rMsEMZaK2699GQtBfWzoKt0KIj7hsYnj2jglR0CWrNpLo0ISIhswHMYvc3u9QFBGY0zvAAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T18:06:30.060923Z"},"content_sha256":"56b9ba44e091e14dbd128238d69cf723e431482c60cad472ee058aae2efb0cf3","schema_version":"1.0","event_id":"sha256:56b9ba44e091e14dbd128238d69cf723e431482c60cad472ee058aae2efb0cf3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N44HF7KPJEIM2MPTVCYWM3HSJE/bundle.json","state_url":"https://pith.science/pith/N44HF7KPJEIM2MPTVCYWM3HSJE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N44HF7KPJEIM2MPTVCYWM3HSJE/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":"2026-05-21T18:06:30Z","links":{"resolver":"https://pith.science/pith/N44HF7KPJEIM2MPTVCYWM3HSJE","bundle":"https://pith.science/pith/N44HF7KPJEIM2MPTVCYWM3HSJE/bundle.json","state":"https://pith.science/pith/N44HF7KPJEIM2MPTVCYWM3HSJE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N44HF7KPJEIM2MPTVCYWM3HSJE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:N44HF7KPJEIM2MPTVCYWM3HSJE","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"098f3b078010a1bd442ac3bf75bccd6ce4193e5c715366589372b935f8e1b894","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T08:32:08Z","title_canon_sha256":"4e76fa505b63076cf7b429a771ff5a7dec93c907c42a680771919304de084bfc"},"schema_version":"1.0","source":{"id":"2605.17325","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17325","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17325v1","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17325","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"pith_short_12","alias_value":"N44HF7KPJEIM","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"pith_short_16","alias_value":"N44HF7KPJEIM2MPT","created_at":"2026-05-20T00:03:52Z"},{"alias_kind":"pith_short_8","alias_value":"N44HF7KP","created_at":"2026-05-20T00:03:52Z"}],"graph_snapshots":[{"event_id":"sha256:a405afc74e07680f52aead6547faf7455fc8f958913b89d5937e76eab3680cc5","target":"graph","created_at":"2026-05-20T00:03:52Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"By utilizing a stateless Pre-Filtering Dispatcher Subsystem (PFDS), in-memory lock-sharded state workers, and a 95% statistical watermark heuristic, our system maintains detection momentum during network partitions to evacuate speculative alerts and achieves total end-to-end operational convergence within a realistic 12-20 seconds window."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The 500,000 events per second synthetic workload and prototype implementation in Go accurately represent the challenges, data patterns, and operational conditions of real-world multi-sector threat detection and collaborative containment."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A federated stream-processing system with PFDS, in-memory sharded workers, and statistical watermarking achieves end-to-end cross-sector threat detection and containment in 12-20 seconds on a 500k events/sec prototype workload."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A federated stream-processing framework detects coordinated cross-sector threats and achieves containment in 12-20 seconds despite network partitions."}],"snapshot_sha256":"c8c70a366143feaa0e85abf553ee347c096fb2962dba788e32aa4e8ad719241a"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"008da2e4dba339413ff533f4fd7057d722b05d839fccdbe5d4f196775de6fa16"},"integrity":{"available":true,"clean":false,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-20T00:01:20.649528Z","status":"completed","version":"1.0.0"},{"findings_count":1,"name":"doi_compliance","ran_at":"2026-05-19T23:52:41.527262Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.813939Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.746834Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.17325/integrity.json","findings":[{"audited_at":"2026-05-19T23:52:41.527262Z","detected_arxiv_id":null,"detected_doi":"10.1109/AC-CESS.2024.3454211","detector":"doi_compliance","finding_type":"recoverable_identifier","note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/AC-CESS.2024.3454211) was visible in the surrounding text but could not be confirmed against doi.org as printed.","ref_index":3,"severity":"advisory","verdict_class":"incontrovertible"}],"snapshot_sha256":"819fbeabc5ce773e2ba3025ee5ebfdceeb50d8be381dc2954610a382dbfb6cbe","summary":{"advisory":1,"by_detector":{"doi_compliance":{"advisory":1,"critical":0,"informational":0,"total":1}},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Critical infrastructure defense is fundamentally bottlenecked by the operational reality that preventive controls are frequently bypassed by sophisticated supply-chain compromises and stolen administrative credentials. When prevention fails, defense relies entirely on rapid, post-ingress threat detection and automated response across sovereign sectors. We present a novel, federated, high-throughput stream-processing and correlation framework designed to detect coordinated cross-sector threat campaigns and orchestrate containment at machine speed. By utilizing a stateless Pre-Filtering Dispatch","authors_text":"Namit Mohale","cross_cats":[],"headline":"A federated stream-processing framework detects coordinated cross-sector threats and achieves containment in 12-20 seconds despite network partitions.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T08:32:08Z","title":"Federated Stream-Processing and Latency-Gated Response for Cross-Sector Threat Detection and Collaborative Containment"},"references":{"count":12,"internal_anchors":0,"resolved_work":12,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"T. Akidau, R. Bradshaw, C. Chambers, S. Chernyak, R.J. Fernández- Moctezuma, R. Lax et al. ‘‘The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbo","work_id":"20c1af1b-06b4-4a52-ae95-94752dac95db","year":2015},{"cited_arxiv_id":"","doi":"10.1109/ictc66702.2025.11388140","is_internal_anchor":false,"ref_index":2,"title":"O. Babayomi and D.-S. Kim. ‘‘Federated Anomaly Detection and Mit- igation for EV Charging Forecasting Under Cyberattacks’’, 2025. /em- phInternational Conference on Information and Communication Tech-","work_id":"e88e7c28-297e-479d-bc0a-a937cb5e33f7","year":2025},{"cited_arxiv_id":"","doi":"10.1109/ac-","is_internal_anchor":false,"ref_index":3,"title":"Enhancing Digital Image Forgery Detection Us- ing Transfer Learning","work_id":"686153ec-89bd-4634-8bdd-4f80ae648276","year":2024},{"cited_arxiv_id":"","doi":"10.1109/ojcs.2025.3618157","is_internal_anchor":false,"ref_index":4,"title":"K. Thirasak, T. Chuaphanngam, D. Chainarong and S. Fugkeaw, ‘‘TF2ML: Threat Filtering With Two-Stage Machine Learning for Effi- cient Provenance-Aware Threat Detection and Response’’,IEEE Open Journal","work_id":"3623392d-a34e-41fd-8b30-8ec1903e7586","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"M. Barni and F. Bartolini,Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications, CRC Press, 2024","work_id":"5611fe6d-d067-49b6-b8b5-49a266b4d537","year":2024}],"snapshot_sha256":"2a64f95d16e43416eba05f490e9689cbf6c29301dec174d2e7fe0a1661da60e1"},"source":{"id":"2605.17325","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T23:44:45.807199Z","id":"1197a7a7-9eef-40cf-86bd-7f484cb221f5","model_set":{"reader":"grok-4.3"},"one_line_summary":"A federated stream-processing system with PFDS, in-memory sharded workers, and statistical watermarking achieves end-to-end cross-sector threat detection and containment in 12-20 seconds on a 500k events/sec prototype workload.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A federated stream-processing framework detects coordinated cross-sector threats and achieves containment in 12-20 seconds despite network partitions.","strongest_claim":"By utilizing a stateless Pre-Filtering Dispatcher Subsystem (PFDS), in-memory lock-sharded state workers, and a 95% statistical watermark heuristic, our system maintains detection momentum during network partitions to evacuate speculative alerts and achieves total end-to-end operational convergence within a realistic 12-20 seconds window.","weakest_assumption":"The 500,000 events per second synthetic workload and prototype implementation in Go accurately represent the challenges, data patterns, and operational conditions of real-world multi-sector threat detection and collaborative containment."}},"verdict_id":"1197a7a7-9eef-40cf-86bd-7f484cb221f5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8a7db32b9a3c9ff396f7ef52b79fcfc0f1cc00601a9880940f2f15968225124d","target":"record","created_at":"2026-05-20T00:03:52Z","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":"098f3b078010a1bd442ac3bf75bccd6ce4193e5c715366589372b935f8e1b894","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T08:32:08Z","title_canon_sha256":"4e76fa505b63076cf7b429a771ff5a7dec93c907c42a680771919304de084bfc"},"schema_version":"1.0","source":{"id":"2605.17325","kind":"arxiv","version":1}},"canonical_sha256":"6f3872fd4f4910cd31f3a8b1666cf2490ed3b33c30ca30b60223f98f173bdc52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f3872fd4f4910cd31f3a8b1666cf2490ed3b33c30ca30b60223f98f173bdc52","first_computed_at":"2026-05-20T00:03:52.221401Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:52.221401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Q4I5sdfAvvwc52dHZya90EPTX7SfKH54J4ZnTVS4pc05PGB3Yyc0B69312ZxDAxDTpNi0TGix6wyiSh6NehIDQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:52.222254Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17325","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:56b9ba44e091e14dbd128238d69cf723e431482c60cad472ee058aae2efb0cf3","sha256:8a7db32b9a3c9ff396f7ef52b79fcfc0f1cc00601a9880940f2f15968225124d","sha256:a405afc74e07680f52aead6547faf7455fc8f958913b89d5937e76eab3680cc5"],"state_sha256":"1bb849834b3c952b3bf9656427900a17dbabe0a85e34515f1e09e2285d92b6b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VkuIMkoFN88xr6DveCoL+/uvq+lnrvfP4btc79IKKZt0xmAjYaHy9vLIxV/+3WRHs4xtywA4m2q7xWY01r9DAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T18:06:30.063899Z","bundle_sha256":"ed35aa81e1a91f68cfece266e3f37fb3148fb6b059323e33282dd235371bdb8c"}}