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Data-Driven Android Malware Analysis Intelligence
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Author(s): Ketaki Anandkumar Pattani (Institute of Advanced Research, Gandhinagar, India)and Sunil Gautam (Institute of Advanced Research, Gandhinagar, India)
Copyright: 2022
Pages: 20
Source title:
Methods, Implementation, and Application of Cyber Security Intelligence and Analytics
Source Author(s)/Editor(s): Jena Om Prakash (Ravenshaw University, India), H.L. Gururaj (Vidyavardhaka College of Engineering, India), M.R. Pooja (Vidyavardhaka College of Engineering, India)and S.P. Pavan Kumar (Vidyavardhaka College of Engineering, India)
DOI: 10.4018/978-1-6684-3991-3.ch011
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Abstract
Android OS powers the majority of the market share. Malware acts as stimuli to the vulnerabilities in Android devices as it affects a huge amount of user data. Users' data is at high risk when it comes to attacks through varied types of malware. Also, mutations in malware have brought up newer variants in them. Malware families have been expanding, thereby making analysis and classification diverse. Mainly classified into static, dynamic, and alternative or hybrid analysis, the field of malware analysis is facing many repercussions. The development of malware is endless and hence calls for intelligent and self-learning approaches in this regard. However, more distinct techniques are in need and can be served by integrating intelligent and analytical capabilities. This chapter involves a fourfold approach with major contributions to review existing Android malware analysis techniques, intelligent techniques for Android malware detection, determination of future challenges and need of security in this direction, and finally, analyzing possible defense mechanisms possible in this regard.
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