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End-to-end malware detection for android IoT devices using deep learning - 2020

End-To-End Malware Detection For Android Iot Devices Using Deep Learning

Research Area:  Machine Learning

Abstract:

The Internet of Things (IoT) has grown rapidly in recent years and has become one of the most active areas in the global market. As an open source platform with a large number of users, Android has become the driving force behind the rapid development of the IoT, also attracted malware attacks. Considering the explosive growth of Android malware in recent years, there is an urgent need to propose efficient methods for Android malware detection. Although the existing Android malware detection methods based on machine learning has achieved encouraging performance, most of these methods require a lot of time and effort from the malware analysts to build dynamic or static features, so these methods are difficult to apply in practice. Therefore, end-to-end malware detection methods without human expert intervention are required. This paper proposes two end-to-end Android malware detection methods based on deep learning. Compared with the existing detection methods, the proposed methods have the advantage of their end-to-end learning process. Our proposed methods resample the raw bytecodes of the classes.dex files of Android applications as input to deep learning models. These models are trained and evaluated in a dataset containing 8K benign applications and 8K malicious applications. Experiments show that the proposed methods can achieve 93.4% and 95.8% detection accuracy respectively. Compared with the existing methods, our proposed methods are not limited by input filesize, no manual feature engineering, low resource consumption, so they are more suitable for application on Android IoT devices.

Keywords:  

Author(s) Name:  Zhongru Ren,Haomin Wu,Qian Ning,Iftikhar Hussain,Bingcai Chen

Journal name:  Ad Hoc Networks

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.adhoc.2020.102098

Volume Information:  Volume 101, 15 April 2020, 102098