A semantic malware detection model based on the GMDH neural networks

Abstract There are several approaches for preventing mobile devices from malware intrusion, but most of them suffer from the insufficient accuracy required for detecting Trojan malware. A combination of semantic and machine learning techniques can be effective in preventing intrusions. In this paper, we have used a hierarchical semantic approach to convert numerical and string data to meaningful values, Subgraph Semantic Homomorphism Coefficient (SSHC) to select optimal features, and Group Method of Data Handling (GMDH) deep neural network (DNN) algorithm to detect malware via a cloud-computing infrastructure. To evaluate our model, Android Trojan Dataset has been used. After evaluation, the accuracy reached 99.91%, which was improved by about 5.25% compared to StormDroid, Drebin, and KuafuDet models. Also, the accuracy was improved by about 10.4% and 31.9% compared to machine learning based approaches of Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), in the state-of-the-art KuafuDet model, respectively.