{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4V3EEADHJRWRFP472VE4QUDVO6","short_pith_number":"pith:4V3EEADH","schema_version":"1.0","canonical_sha256":"e5764200674c6d12bf9fd549c85075778fa24849e468599b91d82af61b35e8c6","source":{"kind":"arxiv","id":"2205.08265","version":1},"attestation_state":"computed","paper":{"title":"A two-steps approach to improve the performance of Android malware detectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Jacques Klein, Kevin Allix, Nadia Daoudi, Tegawend\\'e F. Bissyand\\'e","submitted_at":"2022-05-17T12:04:17Z","abstract_excerpt":"The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that closely resemble legitimate apps. In this paper, we propose GUIDED RETRAINING, a supervised representation learning-based method that boosts the performance of a malware detector. First, the dataset is split into \"easy\" and \"difficult\" samples, where difficulty is associated to the prediction probabilities yielded by a malware detector: for difficult samples, the probabilities are such that the classifier is not conf"},"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":"2205.08265","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-05-17T12:04:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9ed3d9e0ea6527540aaedefdd2d91dfb11e6dff9192d40c06aa256798acc7f13","abstract_canon_sha256":"3c3eebfd82ea83505117bd4755de97e18e04a14c2795ad21c81f500939945ab8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:01.078170Z","signature_b64":"uwsChoVIVpM+6wc1FIY902djBF50XhHSszHr/E0nWZ+E3ATEG5bMfjGTaHsAdwd/Uf5C9hh4IO2WGPvjXrsfDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5764200674c6d12bf9fd549c85075778fa24849e468599b91d82af61b35e8c6","last_reissued_at":"2026-07-05T04:24:01.077813Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:01.077813Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A two-steps approach to improve the performance of Android malware detectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Jacques Klein, Kevin Allix, Nadia Daoudi, Tegawend\\'e F. Bissyand\\'e","submitted_at":"2022-05-17T12:04:17Z","abstract_excerpt":"The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that closely resemble legitimate apps. In this paper, we propose GUIDED RETRAINING, a supervised representation learning-based method that boosts the performance of a malware detector. First, the dataset is split into \"easy\" and \"difficult\" samples, where difficulty is associated to the prediction probabilities yielded by a malware detector: for difficult samples, the probabilities are such that the classifier is not conf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08265","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/2205.08265/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":"2205.08265","created_at":"2026-07-05T04:24:01.077871+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.08265v1","created_at":"2026-07-05T04:24:01.077871+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08265","created_at":"2026-07-05T04:24:01.077871+00:00"},{"alias_kind":"pith_short_12","alias_value":"4V3EEADHJRWR","created_at":"2026-07-05T04:24:01.077871+00:00"},{"alias_kind":"pith_short_16","alias_value":"4V3EEADHJRWRFP47","created_at":"2026-07-05T04:24:01.077871+00:00"},{"alias_kind":"pith_short_8","alias_value":"4V3EEADH","created_at":"2026-07-05T04:24:01.077871+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/4V3EEADHJRWRFP472VE4QUDVO6","json":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6.json","graph_json":"https://pith.science/api/pith-number/4V3EEADHJRWRFP472VE4QUDVO6/graph.json","events_json":"https://pith.science/api/pith-number/4V3EEADHJRWRFP472VE4QUDVO6/events.json","paper":"https://pith.science/paper/4V3EEADH"},"agent_actions":{"view_html":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6","download_json":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6.json","view_paper":"https://pith.science/paper/4V3EEADH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.08265&json=true","fetch_graph":"https://pith.science/api/pith-number/4V3EEADHJRWRFP472VE4QUDVO6/graph.json","fetch_events":"https://pith.science/api/pith-number/4V3EEADHJRWRFP472VE4QUDVO6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6/action/storage_attestation","attest_author":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6/action/author_attestation","sign_citation":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6/action/citation_signature","submit_replication":"https://pith.science/pith/4V3EEADHJRWRFP472VE4QUDVO6/action/replication_record"}},"created_at":"2026-07-05T04:24:01.077871+00:00","updated_at":"2026-07-05T04:24:01.077871+00:00"}