MALWARE RECOGNITION SCHEME FOR ANDROID PLATFORM USING AN OPTIMIZED CNN-BASED APPROACH

Karrar Ahmed Kareem, Ethar Sabah Mohammad Ali
Ministry of Oil, Thiqar Oil Company, Iraq College Of Engineering, Wasit University, Ministry Of Higher Education and Scientific Research, Iraq kalknany526@gmail.com

Abstract

Malware is a malicious code which is developed to harm a computer or network. The number of malwares is growing so fast and this amount of growth makes the computer security researchers invent new methods to protect computers and networks. It is a very serious problem and many efforts are devoted to malware detection in today’s cybersecurity world. In this research, a new method will be proposed to malware detection scheme for Android platform using an Optimized Convolutional Neural Networks based approach, which integrates both risky permission combinations and vulnerable API calls and use them as features in the CNN algorithm.

Keywords:

Malware Detection, Deep Learning, Machine Learning, Security, Classification, Convolutional Neural Networks, Particle Swarm Optimization (PSO).


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References


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