ANNDroid: A Framework for Android Malware Detection Using Feature Selection Techniques and Machine Learning Algorithms

Abstract

Working of android apps depends upon the permissions. While there is an exponential growth of android apps in the last decade, the security of smartphones became a crucial factor. In the literature, academicians and researchers proposed malware detection frameworks based on the principle of simple neural network and regression analysis. In this research article, three artificial intelligence techniques are based on the principle of hybrid approach. Proposed approach are based on functional link artificial neural network (FLANN) with clonal selection algorithm (CSA), particle swarm optimization (PSO) and genetic algorithm (GA), i.e., FLANN-CSA (FCSA), FLANN-PSO (FPSO and MFPSO) and FLANN-genetic (FGA and AFGA). Proposed machine learning techniques are applied on five million distinct android apps. In addition to that, this research article also paid attention toward feature selection techniques such as rough set analysis (RSA) and principal component analysis (PCA) when they are implemented for malware detection. Empirical result reveals that feature reduction approaches are extremely effective in detective malware by employing FLANN-Genetic.