Detection of Malicious Code Variants Based on Deep Learning

TLDR

Malicious code attacks have increased exponentially, and variants pose a key threat to Internet security, yet current detection methods suffer from poor accuracy and speed. The study proposes a deep‑learning approach to improve detection of malware variants. The method transforms malicious code into grayscale images, classifies them with a CNN that automatically extracts features, employs a bat algorithm to mitigate data imbalance among malware families, and is evaluated on Vision Research Lab malware image data. Experiments show the model achieves higher accuracy and faster detection than existing malware detection models.

Abstract

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.