Abstract: In this paper,an abnormal intrusion detection method based on FWKN-SVM (Feature-weighted K-nearest-neighbor Support Vector Machine) for the Android platform was proposed.Firstly,we analyzed the limitations of the traditional SVM in practical applications,and proposed the feature-weighted K-nearest-neighbor method to lessen training set.Then,the system behavior was defined,according to the impact of mobile malware on the system,and the test set and the training set were built by using the data acquisition module implemented on Android phone.Lastly,we used the feature-weighted K-nearest neighbor method to lessen the training set and construct SVM classifier,and then predicted the test set.Simulation result shows that FWKN-SVM classification method has a good performance in Android abnormal intrusion detection.

Key words: Android,SVM,K-nearest-neighbor,Feature-weighted,Lessen training set,Malware