Feature Selection and Classification Based on Swarm Intelligence Approach to Detect Malware in Android Platform
In these years, deep learning methods are used to detect Android malware due to low efficient manual checking and increase in Android malware. Data set quality defines the performance of these models. Low quality of training data set will result in reduced performance. Manual checking guarantees the data set quality in real world. Trained model may cause failure by malicious applications in Google Play. Artificial bee colony (ABC) algorithm based on selective swarm intelligence technique is proposed to rectify this issue, and it is robust detection technique of Android malware. The data set quality will not affect the effectiveness of solution. Better component learner's combination can be computed by genetic algorithm in proposed model, which will enhance the robustness of model. In same area, better robust performance is exhibited by the proposed technique than other methods.