Detection and fine-grained classification of malicious code using convolutional neural networks and swarm intelligence algorithms

Abstract

With the development of society, network security has received more and more attention. Malicious code has also grown, causing network security vulnerabilities and increasing threats to internet security. Therefore, the detection of malicious code becomes very important. However, there are some problems in the current research on malicious code detection, for example, tedious feature extraction and unbalanced data, which is far from the effect people want to achieve. To address these problems, in this paper, we propose a novel malicious code detection and fine-grained classification model by using convolutional neural networks and swarm intelligence algorithms. We converted the binary executable files of malicious codes into greyscale images and then used convolution neural networks to detect and classify malicious codes. In addition, we employed swarm intelligence algorithms to achieve fine-grained classification on unbalanced data in different malicious code families. We conducted a series of experiments on the real malware dataset from Vision Research Lab. The experimental results demonstrated that the proposed solution is effective for fine-grained classification of malicious codes.