Affiliations:
Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil |
Department of Computer Science and Engineering, R.M.K College of Engineering and Technology, Puduvoyal, Gummidipoondi
Correspondence:
[*]
Corresponding author. S. Jebin Bose, Research Scholar, Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil. E-mail: [email protected].
Abstract: In today’s world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The feature selection is carried along with the optimization technique Recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained.
