Android Malware Detection Based on Deep Learning Techniques

To detect Android malware samples, a malware classification model is proposed in this paper. traditional Android malware detection and identification techniques are usually divided into static analysis and dynamic analysis. static analysis of interrelated data sets classification effect is not good, dynamic analysis effect is good but the resource consumption rate is high. In the face of this problem, we made improvements based on static analysis and proposed an artificial intelligence-based solution to achieve the purpose of malicious attack software detection by extracting feature datasets from source code and then using algorithmic models for classification processing and learning. The accuracy of the data was about 94.4% after 200 rounds of training. We used the E-validation set to validate the model, and the accuracy of the E-validation set was 90%. The results of the proposed model have achieved our expected results in terms of classification accuracy.