{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3N2I6PWL2PN4H7TU2VKA4FT5XE","short_pith_number":"pith:3N2I6PWL","schema_version":"1.0","canonical_sha256":"db748f3ecbd3dbc3fe74d5540e167db90efe1566e37e318053c7183f60b7c055","source":{"kind":"arxiv","id":"2606.28917","version":1},"attestation_state":"computed","paper":{"title":"ML-Powered LDAP Reconnaissance Detection using Weak Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Asaf Romano, Avraham Kama, Benjamin Malmberg, Dor Agron, Edward Raff, Michael Brautbar, Sagi Sheinfeld, Shaefer Drew, Yaron Zinar","submitted_at":"2026-06-27T13:48:43Z","abstract_excerpt":"Lightweight Directory Access Protocol (LDAP) is a protocol that allows users to query and modify Active Directory (AD) data. By default, all users have read access to all AD data through LDAP, making it a common initial tool for reconnaissance when a threat actor first compromises an identity. To capture threat actors early in the reconnaissance phase, we developed two machine learning frameworks to detect LDAP reconnaissance: an ML classifier to predict malicious LDAP queries and an ML-based data-mining method to extract malicious query signatures. By correlating LDAP queries with endpoint de"},"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":"2606.28917","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-27T13:48:43Z","cross_cats_sorted":[],"title_canon_sha256":"25a0d1c2afce1034c372eb1338f3ed6c8f1631610520581417a96f5fcbab650e","abstract_canon_sha256":"045936c40c6dd7c094191f20c707675b70fdb7f869d223aa260e15f1d9872b14"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:45.219636Z","signature_b64":"nHYzocfEWJnCm9eDIhdyHcWnUba6Vwpxqs74kxWHDgJw6tq90K+e0ZsmZ/QhJhqbUqCQiLkPARoBYKr4na9PDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db748f3ecbd3dbc3fe74d5540e167db90efe1566e37e318053c7183f60b7c055","last_reissued_at":"2026-06-30T01:17:45.219096Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:45.219096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML-Powered LDAP Reconnaissance Detection using Weak Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Asaf Romano, Avraham Kama, Benjamin Malmberg, Dor Agron, Edward Raff, Michael Brautbar, Sagi Sheinfeld, Shaefer Drew, Yaron Zinar","submitted_at":"2026-06-27T13:48:43Z","abstract_excerpt":"Lightweight Directory Access Protocol (LDAP) is a protocol that allows users to query and modify Active Directory (AD) data. By default, all users have read access to all AD data through LDAP, making it a common initial tool for reconnaissance when a threat actor first compromises an identity. To capture threat actors early in the reconnaissance phase, we developed two machine learning frameworks to detect LDAP reconnaissance: an ML classifier to predict malicious LDAP queries and an ML-based data-mining method to extract malicious query signatures. By correlating LDAP queries with endpoint de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28917","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/2606.28917/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":"2606.28917","created_at":"2026-06-30T01:17:45.219186+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.28917v1","created_at":"2026-06-30T01:17:45.219186+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28917","created_at":"2026-06-30T01:17:45.219186+00:00"},{"alias_kind":"pith_short_12","alias_value":"3N2I6PWL2PN4","created_at":"2026-06-30T01:17:45.219186+00:00"},{"alias_kind":"pith_short_16","alias_value":"3N2I6PWL2PN4H7TU","created_at":"2026-06-30T01:17:45.219186+00:00"},{"alias_kind":"pith_short_8","alias_value":"3N2I6PWL","created_at":"2026-06-30T01:17:45.219186+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/3N2I6PWL2PN4H7TU2VKA4FT5XE","json":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE.json","graph_json":"https://pith.science/api/pith-number/3N2I6PWL2PN4H7TU2VKA4FT5XE/graph.json","events_json":"https://pith.science/api/pith-number/3N2I6PWL2PN4H7TU2VKA4FT5XE/events.json","paper":"https://pith.science/paper/3N2I6PWL"},"agent_actions":{"view_html":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE","download_json":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE.json","view_paper":"https://pith.science/paper/3N2I6PWL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.28917&json=true","fetch_graph":"https://pith.science/api/pith-number/3N2I6PWL2PN4H7TU2VKA4FT5XE/graph.json","fetch_events":"https://pith.science/api/pith-number/3N2I6PWL2PN4H7TU2VKA4FT5XE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE/action/storage_attestation","attest_author":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE/action/author_attestation","sign_citation":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE/action/citation_signature","submit_replication":"https://pith.science/pith/3N2I6PWL2PN4H7TU2VKA4FT5XE/action/replication_record"}},"created_at":"2026-06-30T01:17:45.219186+00:00","updated_at":"2026-06-30T01:17:45.219186+00:00"}