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Leveraging the first line of defense: a study on the evolution and usage of android security permissions for enhanced android malware detection.

, , and . J. Comput. Virol. Hacking Tech., 19 (1): 65-96 (March 2023)

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Corrigendum to Concept drift and cross-device behavior: Challenges and implications for effective android malware detection Computers & Security, Volume 120, 102757., , and . Comput. Secur., (2023)On the Application of Active Learning to Handle Data Evolution in Android Malware Detection., and . ICDF2C, volume 508 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 256-273. Springer, (2022)Towards the Integration of a Post-Hoc Interpretation Step into the Machine Learning Workflow for IoT Botnet Detection., , and . ICMLA, page 1162-1169. IEEE, (2019)KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization., , and . Comput. Secur., (2021)Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives., , , and . TML4H, volume 13932 of Lecture Notes in Computer Science, page 25-40. Springer, (2023)Using MedBIoT Dataset to Build Effective Machine Learning-Based IoT Botnet Detection Systems., , , and . ICISSP (Revised Selected Papers), volume 1545 of Communications in Computer and Information Science, page 222-243. Springer, (2020)In-depth Feature Selection and Ranking for Automated Detection of Mobile Malware., , and . ICISSP, page 274-283. SciTePress, (2019)Network IDS alert classification with active learning techniques., and . J. Inf. Secur. Appl., (2024)Stream clustering guided supervised learning for classifying NIDS alerts., and . Future Gener. Comput. Syst., (2024)Android malware concept drift using system calls: Detection, characterization and challenges., , and . Expert Syst. Appl., (2022)