{"url":"/task/android-malware-detection","name":"Android Malware Detection","slug":"android-malware-detection","description_markdown":null,"categories":[{"name":"Miscellaneous","url":"/area/miscellaneous"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":76,"papers_with_code":19,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":1,"subtasks":0,"parent_tasks":1},"benchmarks":[],"datasets":[{"url":"/dataset/appdroid","name":"Appdroid","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/malware-classification","name":"Malware Classification"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":19,"of":19,"tagged_in_all":76,"items":[{"url":"/paper/continuous-learning-for-android-malware","title":"Continuous Learning for Android Malware Detection","date":"2023-02-08","arxiv_id":"2302.04332","repositories_listed":2,"syntology":null},{"url":"/paper/evaluating-the-robustness-of-adversarial","title":"Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems","date":"2025-05-14","arxiv_id":"2505.09342","repositories_listed":1,"syntology":null},{"url":"/paper/crystal-ball-from-innovative-attacks-to","title":"Crystal ball: From innovative attacks to attack effectiveness classifier","date":"2024-12-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/maskdroid-robust-android-malware-detection","title":"MASKDROID: Robust Android Malware Detection with Masked Graph Representations","date":"2024-09-29","arxiv_id":"2409.19594","repositories_listed":1,"syntology":null},{"url":"/paper/detectbert-towards-full-app-level","title":"DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware","date":"2024-08-29","arxiv_id":"2408.16353","repositories_listed":1,"syntology":null},{"url":"/paper/improving-adversarial-robustness-in-android","title":"Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations","date":"2024-08-27","arxiv_id":"2408.16025","repositories_listed":1,"syntology":null},{"url":"/paper/malpurifier-enhancing-android-malware","title":"MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks","date":"2023-12-11","arxiv_id":"2312.06423","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-concept-drift-handling-for-batch","title":"Efficient Concept Drift Handling for Batch Android Malware Detection Models","date":"2023-09-18","arxiv_id":"2309.09807","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-query-based-attack-against-ml-based","title":"Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting","date":"2023-09-05","arxiv_id":"2309.01866","repositories_listed":1,"syntology":null},{"url":"/paper/domain-constraints-in-feature-space","title":"Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection","date":"2022-05-30","arxiv_id":"2205.15128","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-fair-comparison-and-realistic","title":"Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning","date":"2022-05-25","arxiv_id":"2205.12569","repositories_listed":1,"syntology":null},{"url":"/paper/mamadroid2-0-the-holes-of-control-flow-graphs","title":"MaMaDroid2.0 -- The Holes of Control Flow Graphs","date":"2022-02-28","arxiv_id":"2202.13922","repositories_listed":1,"syntology":null},{"url":"/paper/evadedroid-a-practical-evasion-attack-on","title":"EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection","date":"2021-10-07","arxiv_id":"2110.03301","repositories_listed":1,"syntology":null},{"url":"/paper/can-we-leverage-predictive-uncertainty-to","title":"Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?","date":"2021-09-20","arxiv_id":"2109.09654","repositories_listed":1,"syntology":null},{"url":"/paper/dexray-a-simple-yet-effective-deep-learning","title":"DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode","date":"2021-09-05","arxiv_id":"2109.03326","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-temporal-graph-transformer-an","title":"heterogeneous temporal graph transformer: an intelligent system for evolving android malware detection","date":"2021-08-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-android-malware-defenses-a","title":"Deep Learning for Android Malware Defenses: a Systematic Literature Review","date":"2021-03-09","arxiv_id":"2103.05292","repositories_listed":1,"syntology":null},{"url":"/paper/why-an-android-app-is-classified-as-malware","title":"Why an Android App is Classified as Malware? Towards Malware Classification Interpretation","date":"2020-04-24","arxiv_id":"2004.11516","repositories_listed":1,"syntology":null},{"url":"/paper/androdet-an-adaptive-android-obfuscation","title":"AndrODet: An Adaptive Android Obfuscation Detector","date":"2019-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}