{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NOQG2WS7QCY3UQN4T26LIYMTOK","short_pith_number":"pith:NOQG2WS7","schema_version":"1.0","canonical_sha256":"6ba06d5a5f80b1ba41bc9ebcb46193728b1ed7dc540738a683833fad0b1a9e0f","source":{"kind":"arxiv","id":"2412.09641","version":1},"attestation_state":"computed","paper":{"title":"Machine Learning Driven Smishing Detection Framework for Mobile Security","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Ankit Kumar Jain, Diksha Goel, Hussain Ahmad, Nikhil Kumar Goel","submitted_at":"2024-12-09T08:20:20Z","abstract_excerpt":"The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS language, which includes abbreviations, slang, and short forms. This paper presents an enhanced content-based smishing detection framework that leverages advanced text normalization techniques to improve detection accuracy. By converting nonstanda"},"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":"2412.09641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2024-12-09T08:20:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a59248e9a3deb693e42126707a681c765ef0e8d1fc31991537515a6ba7a00a36","abstract_canon_sha256":"d80d3bc45e03045a69277395ce24a1b1f6aeff992f2e0cf9646ef599b25fbb94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:48:39.408804Z","signature_b64":"McrRAm6uI8+WwRPc2qX0XPcGk7pngPu/nHAvO8u8yZ5tzNVyecISoB/cBKUBEd7n1rLqEdOzTwmnROkZCSQdBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ba06d5a5f80b1ba41bc9ebcb46193728b1ed7dc540738a683833fad0b1a9e0f","last_reissued_at":"2026-07-05T09:48:39.408377Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:48:39.408377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning Driven Smishing Detection Framework for Mobile Security","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Ankit Kumar Jain, Diksha Goel, Hussain Ahmad, Nikhil Kumar Goel","submitted_at":"2024-12-09T08:20:20Z","abstract_excerpt":"The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS language, which includes abbreviations, slang, and short forms. This paper presents an enhanced content-based smishing detection framework that leverages advanced text normalization techniques to improve detection accuracy. By converting nonstanda"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.09641","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/2412.09641/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":"2412.09641","created_at":"2026-07-05T09:48:39.408434+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.09641v1","created_at":"2026-07-05T09:48:39.408434+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.09641","created_at":"2026-07-05T09:48:39.408434+00:00"},{"alias_kind":"pith_short_12","alias_value":"NOQG2WS7QCY3","created_at":"2026-07-05T09:48:39.408434+00:00"},{"alias_kind":"pith_short_16","alias_value":"NOQG2WS7QCY3UQN4","created_at":"2026-07-05T09:48:39.408434+00:00"},{"alias_kind":"pith_short_8","alias_value":"NOQG2WS7","created_at":"2026-07-05T09:48:39.408434+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01739","citing_title":"AgenticVM: Agentic AI for Adaptive Software Vulnerability Management","ref_index":56,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10316","citing_title":"Comparative Analysis of Large Language Models in Healthcare","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04442","citing_title":"Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK","json":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK.json","graph_json":"https://pith.science/api/pith-number/NOQG2WS7QCY3UQN4T26LIYMTOK/graph.json","events_json":"https://pith.science/api/pith-number/NOQG2WS7QCY3UQN4T26LIYMTOK/events.json","paper":"https://pith.science/paper/NOQG2WS7"},"agent_actions":{"view_html":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK","download_json":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK.json","view_paper":"https://pith.science/paper/NOQG2WS7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.09641&json=true","fetch_graph":"https://pith.science/api/pith-number/NOQG2WS7QCY3UQN4T26LIYMTOK/graph.json","fetch_events":"https://pith.science/api/pith-number/NOQG2WS7QCY3UQN4T26LIYMTOK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK/action/storage_attestation","attest_author":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK/action/author_attestation","sign_citation":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK/action/citation_signature","submit_replication":"https://pith.science/pith/NOQG2WS7QCY3UQN4T26LIYMTOK/action/replication_record"}},"created_at":"2026-07-05T09:48:39.408434+00:00","updated_at":"2026-07-05T09:48:39.408434+00:00"}