@inproceedings{1faddbd9cb2e4195b10ebb2cef43dc6e,
title = "Evaluating Machine Learning Models for Android Malware Detection: A Comparison Study",
abstract = "Android is the most popular mobile operating system having billions of active users worldwide that attracted advertisers, hackers, and cybercriminals to develop malware for various purposes. In recent years, wide-ranging researches have been conducted on malware analysis and detection for Android devices while Android has also implemented various security controls to deal with the malware problems, including unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. In this paper, we optimize and evaluate different types of machine learning algorithms by implementing a classifier based on static analysis in order to detect malware in applications running on the Android OS. In our evaluation, we use 11,120 applications with 5,560 malware samples and 5,560 benign samples of the DREBIN dataset, and the accuracy we achieved is higher than 94\%; therefore, the study has demonstrated the effectiveness of using machine learning classifiers for detecting Android malware.",
keywords = "Classifier, DREBIN, Google Play, Machine Learning, Malware, Optimization",
author = "Rana, \{Md Shohel\}",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.",
year = "2018",
month = dec,
day = "14",
doi = "10.1145/3301326.3301390",
language = "English",
isbn = "978-1-4503-6553-6",
series = "Proceedings of the 2018 VII International Conference on Network, Communication and Computing",
pages = "17--21",
booktitle = "Proceedings of 2018 7th International Conference on Network, Communication and Computing, ICNCC 2018",
}