Abstract: To solve the problem of low detection rate of traditional Android malware detection methods,an Android malware detection method based on deep contractive denoising autoencoder network (DCDAN) was proposed.Firstly,the APK file is analyzed in reverse to obtain seven kinds of information in the APK file,such as permissions,sensitive API in the file,which are taken as feature attributes.Then,the feature attributes are taken as the input of the deep contractive denoising autoencoder network,train each contractive denoising autoencoder network is trined layer by layer from bottom to top by using greedy algorithm,and the The deep contractive denoising autoencoder network completed by training is used to extract the information of the original features to obtain the optimal low-dimensional representation.Finally,the back propagation algorithm is used to train and classify the acquired low-dimensional representations to realize the detection of Android malware.Adding noise to the input data of the deep autoencoder network makes the reconstructed data more robust,and adding jacobian matrix as penalty term enhances the anti-disturbance ability of the deep autoencoder network.The experimental results verify the feasibility and high efficiency of this method.Compared with the traditional detection method,the detection method can improve the accuracy of malware detection and reduce the false alarm rate effectively.

Key words: Android malware, Back propagation algorithm, Deep contractive denoising autoencoder network, Greedy algorithm, Jacobian matrix