Machine Learning for Android Malware Detection Using Permission and API Calls

By Naser Peiravian, Xingquan Zhu. IEEE International Conference on Tools with Artificial Intelligence, 2013

351 citations recorded by the source corpus.

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Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection (2018, background): “based classifiers to categorize applications as either malicious or benign [1], [2], [21], [25], [32].”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Using permission and API call, SVM, J48, and Bagging were used to detect malware in Android-based applications and obtained 96.39% accuracy with bagging [620].”
A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, background): “In Peiravian and Zhu (2013), malicious Android apps are classified using API calls and permissions.”
A Survey of App Store Analysis for Software Engineering (2017, methodology): “[73], 2013 g MobiSys 265,359 Peiravian and Xingquan [180],’13 g ICTAI 1,250 Chakradeo et al.”
DL-Droid: Deep learning based android malware detection using real devices (2019, background): “Arp et al. (2014) ; Dini et al. (2012) ; Peiravian and Zhu (2013) ; Rasthofer et al. (2014) ; Shabtai et al. (2012) ; Yerima et al. (2015b, 2016b) are examples of published research that apply machine learning techniques to detect zero-day Android malware.”