{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:VILEF2DMKEUB6UNNMBNH7P3KI3","short_pith_number":"pith:VILEF2DM","schema_version":"1.0","canonical_sha256":"aa1642e86c51281f51ad605a7fbf6a46e919460166e4db48bebe74584c453156","source":{"kind":"arxiv","id":"2005.06599","version":1},"attestation_state":"computed","paper":{"title":"Phishing URL Detection Through Top-level Domain Analysis: A Descriptive Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"cs.CR","authors_text":"Nikolaos Pitropakis, Orestis Christou, Pavlos Papadopoulos, Sean McKeown, William J. Buchanan","submitted_at":"2020-05-13T21:41:29Z","abstract_excerpt":"Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate. Even with adequate training and high situational awareness, it can still be hard for users to continually be aware of the URL of the website they are visiting. Traditional detection methods rely on blocklists and content analysis, both of which require time-consuming human verification. Thus, there have been attempts focusing on the predictive filtering of such URLs. This study aims to develop a machine-learning model to detect fraudulent URLs which can be u"},"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":"2005.06599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2020-05-13T21:41:29Z","cross_cats_sorted":["cs.CY","cs.LG"],"title_canon_sha256":"d868dc012a31eb2439f4aa6c1621f57e0702bc28dcb1a8da62b38dc59a5f46e4","abstract_canon_sha256":"afc1c9dad266320961bc984a6d88618a10876dac2104b538b34d6c00ed9b0100"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:48.420694Z","signature_b64":"R/BZOkp9PV6LqIwv2Y8TGjFA1KYxDrMLfuZ/gKDY7GYWwE7T/WWl2IHtX5WOgtCNj05YDrGt/CzM9hSGU5B7AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa1642e86c51281f51ad605a7fbf6a46e919460166e4db48bebe74584c453156","last_reissued_at":"2026-07-05T01:02:48.420240Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:48.420240Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Phishing URL Detection Through Top-level Domain Analysis: A Descriptive Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"cs.CR","authors_text":"Nikolaos Pitropakis, Orestis Christou, Pavlos Papadopoulos, Sean McKeown, William J. Buchanan","submitted_at":"2020-05-13T21:41:29Z","abstract_excerpt":"Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate. Even with adequate training and high situational awareness, it can still be hard for users to continually be aware of the URL of the website they are visiting. Traditional detection methods rely on blocklists and content analysis, both of which require time-consuming human verification. Thus, there have been attempts focusing on the predictive filtering of such URLs. This study aims to develop a machine-learning model to detect fraudulent URLs which can be u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.06599","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/2005.06599/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":"2005.06599","created_at":"2026-07-05T01:02:48.420307+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.06599v1","created_at":"2026-07-05T01:02:48.420307+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.06599","created_at":"2026-07-05T01:02:48.420307+00:00"},{"alias_kind":"pith_short_12","alias_value":"VILEF2DMKEUB","created_at":"2026-07-05T01:02:48.420307+00:00"},{"alias_kind":"pith_short_16","alias_value":"VILEF2DMKEUB6UNN","created_at":"2026-07-05T01:02:48.420307+00:00"},{"alias_kind":"pith_short_8","alias_value":"VILEF2DM","created_at":"2026-07-05T01:02:48.420307+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.11289","citing_title":"LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3","json":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3.json","graph_json":"https://pith.science/api/pith-number/VILEF2DMKEUB6UNNMBNH7P3KI3/graph.json","events_json":"https://pith.science/api/pith-number/VILEF2DMKEUB6UNNMBNH7P3KI3/events.json","paper":"https://pith.science/paper/VILEF2DM"},"agent_actions":{"view_html":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3","download_json":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3.json","view_paper":"https://pith.science/paper/VILEF2DM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.06599&json=true","fetch_graph":"https://pith.science/api/pith-number/VILEF2DMKEUB6UNNMBNH7P3KI3/graph.json","fetch_events":"https://pith.science/api/pith-number/VILEF2DMKEUB6UNNMBNH7P3KI3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3/action/storage_attestation","attest_author":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3/action/author_attestation","sign_citation":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3/action/citation_signature","submit_replication":"https://pith.science/pith/VILEF2DMKEUB6UNNMBNH7P3KI3/action/replication_record"}},"created_at":"2026-07-05T01:02:48.420307+00:00","updated_at":"2026-07-05T01:02:48.420307+00:00"}