DroidSieve
Author:
Affiliation:
1. Royal Holloway, University of London, Egham, United Kingdom
2. University of Cagliari, Cagliari, Italy
Funder
Engineering and Physical Sciences Research Council
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3029806.3029825
Reference39 articles.
1. DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
2. Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
3. OpSeq
4. Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
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1. COMEX: Deeply Observing Application Behavior on Real Android Devices;Proceedings of the 17th Cyber Security Experimentation and Test Workshop;2024-08-13
2. Image-based detection and classification of Android malware through CNN models;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30
3. Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey;Computers and Electrical Engineering;2024-07
4. How to Circumvent and Beat the Ransomware in Android Operating System—A Case Study of Locker.CB!tr;Electronics;2024-06-06
5. PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection;Scientific Reports;2024-05-10
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