Separating Malicious from Benign Software Using Deep Learning Algorithm

Abstract

The increased usage of the Internet raises cyber security attacks in digital environments. One of the largest threats that initiate cyber attacks is malicious software known as malware. Automatic creation of malware as well as obfuscation and packing techniques make the malicious detection processes a very challenging task. The obfuscation techniques allow malware variants to bypass most of the leading literature malware detection methods. In this paper, a more effective malware detection system is proposed. The goal of the study is to detect traditional as well as new and complex malware variants. The proposed approach consists of three modules. Initially, the malware samples are collected and analyzed by using dynamic malware analysis tools, and execution traces are collected. Then, the collected system calls are used to create malware behaviors as well as features. Finally, a proposed deep learning methodology is used to effectively separate malware from benign samples. The deep learning methodology consists of one input layer, three hidden layers, and an output layer. In hidden layers, 500, 64, and 32 fully connected neurons are used in the first, second, and third hidden layers, respectively. To keep the model simple as well as obtain optimal solutions, we have selected three hidden layers in which neurons are decreasing in the following subsequent layers. To increase the model performance and use more important features, various activation functions are used. The test results show that the proposed system can effectively detect the malware with more than 99% DR, f-measure, and 99.80 accuracy, which is substantially high when compared with other methods. The proposed system can recognize new malware variants that could not be detected with signature, heuristic, and some behavior-based detection techniques. Further, the proposed system has performed better than the well-known methods that are mentioned in the literature based on the DR, precision, recall, f-measure, and accuracy metrics.

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