Abstract
The Android platform has become the most common mobile platform of smart mobile devices that attracts many users, developers and vendors. Accordingly, millions of Android applications have been created to offer many functionalities and services to users. However, the fast growth rate of such applications has led to a huge increase in the development and spread of Android malware applications by cyber attackers and criminals. In order to overcome the difficulties faced by the conventional signature-based methods, this paper suggests hybrid intelligent Android malware detection approaches based on evolving support vector machine with evolutionary algorithms in order to enhance Android malware detection. In the proposed hybrid intelligent evolving approaches, the optimization problem in support vector machine is solved using a genetic algorithm (GA) and a particle swarm optimization (PSO), referred to as Droid-HESVMGA and Droid-HESVMPSO, in order to help in increasing the accuracy of the Android malware detection. The experimental results showed that the proposed Droid-HESVMGA and Droid-HESVMPSO approaches achieved the best detection results and substantially outperformed the most popular machine learning classifiers and other existing hybrid malware detection approaches.