Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support\nvector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm\noptimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the\nPSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover,\nto overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive\ninertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term.The goal is to assign\nsuitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that\nthe IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than\nthat of the IG weights.
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