{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IPVLP7HLK7OUMEGTI2B4W5J2DY","short_pith_number":"pith:IPVLP7HL","schema_version":"1.0","canonical_sha256":"43eab7fceb57dd4610d34683cb753a1e3d526da2ae6c50adfad87b11bc6cd7c7","source":{"kind":"arxiv","id":"2507.22231","version":1},"attestation_state":"computed","paper":{"title":"Understanding Concept Drift with Deprecated Permissions in Android Malware Detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Ahmed Sabbah, David Mohaisen, Radi Jarrar, Samer Zein","submitted_at":"2025-07-29T20:54:48Z","abstract_excerpt":"Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning (ML) algorithms have been applied to Android malware detection using permission-based features and feature selection techniques, often achieving high accuracy. However, these studies have largely overlooked important factors such as protection levels and the deprecation or restriction of permissions due to updates in the Android OS -- factors that "},"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":"2507.22231","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-07-29T20:54:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ca75ad2803ba2c188e1e654a602ad5e44a62c9fc1da21d025e962f1b2b698335","abstract_canon_sha256":"6d3a79c12aefc78b738b035b1ce4e983dd43dd3c4d7fd06ed754df1487b542d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:18.921247Z","signature_b64":"rhgDdvt9r0R6E2VLDWxoqmjzizQB9ZwMINiwndZxUwcZ/2Lqg3ryUq0Jn3HcrCFOaqvxpUM6A8G0qWfKVaM+AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43eab7fceb57dd4610d34683cb753a1e3d526da2ae6c50adfad87b11bc6cd7c7","last_reissued_at":"2026-07-05T11:45:18.920734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:18.920734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding Concept Drift with Deprecated Permissions in Android Malware Detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Ahmed Sabbah, David Mohaisen, Radi Jarrar, Samer Zein","submitted_at":"2025-07-29T20:54:48Z","abstract_excerpt":"Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning (ML) algorithms have been applied to Android malware detection using permission-based features and feature selection techniques, often achieving high accuracy. However, these studies have largely overlooked important factors such as protection levels and the deprecation or restriction of permissions due to updates in the Android OS -- factors that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.22231","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/2507.22231/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":"2507.22231","created_at":"2026-07-05T11:45:18.920796+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.22231v1","created_at":"2026-07-05T11:45:18.920796+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.22231","created_at":"2026-07-05T11:45:18.920796+00:00"},{"alias_kind":"pith_short_12","alias_value":"IPVLP7HLK7OU","created_at":"2026-07-05T11:45:18.920796+00:00"},{"alias_kind":"pith_short_16","alias_value":"IPVLP7HLK7OUMEGT","created_at":"2026-07-05T11:45:18.920796+00:00"},{"alias_kind":"pith_short_8","alias_value":"IPVLP7HL","created_at":"2026-07-05T11:45:18.920796+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23623","citing_title":"Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY","json":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY.json","graph_json":"https://pith.science/api/pith-number/IPVLP7HLK7OUMEGTI2B4W5J2DY/graph.json","events_json":"https://pith.science/api/pith-number/IPVLP7HLK7OUMEGTI2B4W5J2DY/events.json","paper":"https://pith.science/paper/IPVLP7HL"},"agent_actions":{"view_html":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY","download_json":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY.json","view_paper":"https://pith.science/paper/IPVLP7HL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.22231&json=true","fetch_graph":"https://pith.science/api/pith-number/IPVLP7HLK7OUMEGTI2B4W5J2DY/graph.json","fetch_events":"https://pith.science/api/pith-number/IPVLP7HLK7OUMEGTI2B4W5J2DY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY/action/storage_attestation","attest_author":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY/action/author_attestation","sign_citation":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY/action/citation_signature","submit_replication":"https://pith.science/pith/IPVLP7HLK7OUMEGTI2B4W5J2DY/action/replication_record"}},"created_at":"2026-07-05T11:45:18.920796+00:00","updated_at":"2026-07-05T11:45:18.920796+00:00"}