Android Malware Detection Using Hybrid-Based Analysis & Deep Neural Network

Android Malware Detection Using Hybrid-Based Analysis & Deep Neural Network

연구 분야: Safety

학회: 2020 3rd International Conference on Information and Communications Technology (ICOIACT)

Currently, the growth of the Android operating system on smartphone devices is proliferating. A comfort that is felt by the community in using smartphones in various activities such as communication, playing games, and other things driving the popularity of smartphone use. However, the Android platform is now a target opportunity for cybercrime against security threats such as malicious software or malware. Identifying this malware is very important to maintain user security and privacy. However, due to the increasingly complicated malware identification process, it is necessary to use deep learning for malware classification. This study compiles static and dynamic analysis features from benign and malicious applications. The features extracted from APK consists of the API call sequence, system command, manifest permission, and intent. We then process that data using a deep neural network. We also concentrated on maximizing achievement tuning several configurations to assure the best combination of the hyper-parameters and reach the highest statistical metric value. Experimental results show that our model reached 99.08% accuracy, 98.14% recall, and 99.54% precision.