A semantic malware detection model based on the GMDH neural networks. (May 2021)

• Record Type: Journal Article
• Authors: Shahidi, Seyed Mehdi; Shakeri, Hassan; Jalali, Mehrdad
• Is Part Of: Computers & electrical engineering. Volume 91(2021)
• Publication Date: 2021-05
• Subjects: Trojan malware -- Semantic hierarchy -- Subgraphs Semantic Homomorphism Coefficient (SSHC) -- Group Method of Data Handling (GMDH) -- Deep Neural Network (DNN) -- Cloud computing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107099
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Trojan malware detection in the cloud-computing infrastructure. Using semantic algorithms to detect malware, in particular unknown ones. Ability to detect malicious mobile malware with very high accuracy. Using subgraphs semantic homeomorphism coefficient innovation to classify Trojans. Combining DNN GMDH algorithm and semantic methods to detect malware automatically. 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. Graphical abstract: Image, graphical abstract