Abstract: With the rapid development of the Internet and mobile terminals,there are a lot of important information stored in mobile phones.An important way to ensure that these information is not compromised is to detect and process malicious applications in mobile phones.Before detecting malicious applications,feature extraction is required for samples,and how to effectively select features among many features is a crucial process in malicious application detection.Based on the application of Android platform,this paper established an Android malicious application detection model based on the improved artificial bee colony algorithm.By effectively selecting the features,the feature combination that optimizes the classification results is finally obtained,thereby improving the detection performance of Android malicious application detection.The Android application features are extracted under static and dynamic conditions respectively.The malicious application detection model is tested by various classification algorithms.It is proved that the proposed malicious malicious detection method based on the improved artificial bee colony algorithm has the feasibility and superiority.

Key words: Artificial bee colony classification, Feature optimization, Feature selection, Malicious application detection