ABSTRACT
Smartphone technology has seen an unprecedented growth in production because of its services to consumers, ranging from telephony to mobile Internet. Among the major market players in the smartphone operating systems, Android claims a major portion of the market share. However, malicious code writers have penetrated into the Android environment. This necessitates malware detection and mitigation mechanisms in the Android environment. To date, various approaches have been made to detect malware in the Android environment. This chapter presents a comprehensive review on malicious software (malware) detection and mitigation in the Android environment-one of the important security threats prevailing in the environment. It begins with a brief taxonomy of the currently available mechanisms to detect and/or thwart the malware in the mobile environment in general, and in the Android environment in particular. Furthermore, the chapter outlines current solutions for malware detection and mitigation mechanisms in the Android environment in detail. It covers important dimensions of these solutions that include generic malware detection approaches, signature-based approaches, feature extraction-based approach, machine learning (ML)-based schemes, permission-based techniques, and data-and text mining-based malware detection techniques. Because malware families are constantly growing, newmechanisms are needed to meet the challenges of malware detection and mitigation in the Android environment. New directions for malware detection techniques in the Android environment have also been summarized. We argue that, thanks to the growing potential of big data analysis-based techniques, malware detection based on big data and cloud computing for Android will likely outperform the existing techniques because of its capability of handling a large volume of data, efficiency, and robustness.
