Abstract:  In order to improve the efficiency of Android malware application detection, the binary particle swarm optimization (BPSO) is used for optimal selection of complete ensemble of original features, combined with the Nave Bayesian (NB) classification algorithm，an Android malware detection method based on BPSO-NB algorithm is proposed. First, this method uses static analysis for unknown applications to extract the permission information in an AndroidManifest.XML file as a feature. Then, it uses the BPSO algorithm to optimize selected classification feature,  and uses the classification accuracy of  NB algorithm as the evaluation function. Finally, NB classification algorithm is used to construct classifier for Android malicious applications. Through cross experiment, BPSO-NB classification equipment has higher classification accuracy, and the optimal selection of BPSO algorithm classification characteristics under the condition of the security classification accuracy can effectively improve the efficiency of detection.

Key words:  binary particle swarm, Nave Bayesian, feature selection, malware application detection, static analysis