{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:KL2RWIPTUZJDJNXVQCVLRCAXW3","short_pith_number":"pith:KL2RWIPT","schema_version":"1.0","canonical_sha256":"52f51b21f3a65234b6f580aab88817b6ce4dc91b3961449c24ad8a6ac34b4974","source":{"kind":"arxiv","id":"1611.00791","version":1},"attestation_state":"computed","paper":{"title":"Predicting Domain Generation Algorithms with Long Short-Term Memory Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Anjum Ahuja, Daniel Grant, Hyrum S. Anderson, Jonathan Woodbridge","submitted_at":"2016-11-02T20:34:56Z","abstract_excerpt":"Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In order to block DGA C&C traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generating a list of domains for a given seed. The domains are then either preregistered or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors using a large number of seeds in algorithms with multivariate recurrence properti"},"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":"1611.00791","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2016-11-02T20:34:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"747dc3451ec4f494f63f8cb9b9a2b70013413c0bc103af150e727425fede8aaa","abstract_canon_sha256":"70e5cd30be25aa26abd7f23402e7e4b89b6686341c088070c9fe0cb2c696c4e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:18.738353Z","signature_b64":"BL0tVEHQhRYg9LshzIFQ1k/g1yW4VxjIt+F/nQ6k1lan/vygyFIQ7OQjZb5hnP7vmx7A3CPwSoqFrTlpdE0jCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52f51b21f3a65234b6f580aab88817b6ce4dc91b3961449c24ad8a6ac34b4974","last_reissued_at":"2026-05-18T01:00:18.737611Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:18.737611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Domain Generation Algorithms with Long Short-Term Memory Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Anjum Ahuja, Daniel Grant, Hyrum S. Anderson, Jonathan Woodbridge","submitted_at":"2016-11-02T20:34:56Z","abstract_excerpt":"Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In order to block DGA C&C traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generating a list of domains for a given seed. The domains are then either preregistered or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors using a large number of seeds in algorithms with multivariate recurrence properti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00791","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":"1611.00791","created_at":"2026-05-18T01:00:18.737719+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.00791v1","created_at":"2026-05-18T01:00:18.737719+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00791","created_at":"2026-05-18T01:00:18.737719+00:00"},{"alias_kind":"pith_short_12","alias_value":"KL2RWIPTUZJD","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"KL2RWIPTUZJDJNXV","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"KL2RWIPT","created_at":"2026-05-18T12:30:25.849896+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.08276","citing_title":"An AI-based, Multi-stage detection system of banking botnets","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10436","citing_title":"DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3","json":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3.json","graph_json":"https://pith.science/api/pith-number/KL2RWIPTUZJDJNXVQCVLRCAXW3/graph.json","events_json":"https://pith.science/api/pith-number/KL2RWIPTUZJDJNXVQCVLRCAXW3/events.json","paper":"https://pith.science/paper/KL2RWIPT"},"agent_actions":{"view_html":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3","download_json":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3.json","view_paper":"https://pith.science/paper/KL2RWIPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.00791&json=true","fetch_graph":"https://pith.science/api/pith-number/KL2RWIPTUZJDJNXVQCVLRCAXW3/graph.json","fetch_events":"https://pith.science/api/pith-number/KL2RWIPTUZJDJNXVQCVLRCAXW3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3/action/storage_attestation","attest_author":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3/action/author_attestation","sign_citation":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3/action/citation_signature","submit_replication":"https://pith.science/pith/KL2RWIPTUZJDJNXVQCVLRCAXW3/action/replication_record"}},"created_at":"2026-05-18T01:00:18.737719+00:00","updated_at":"2026-05-18T01:00:18.737719+00:00"}