CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

Abstract

Malware is a significant threat to the field of cyber security. There is a wide variety of malware, which can be programmed to threaten computer security by exploiting various networks, operating systems, software and physical security vulnerabilities. So, detecting malware has become a significant part of maintaining network security. In this paper, data enhancement techniques are used in the data preprocessing stage, then a novel detection mode—CPL-Net employing malware texture image—is proposed. The model consists of a feature extraction component, a feature fusion component and a classification component, the core of which is based on the parallel fusion of spatio-temporal features by Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). Through experiments, it has been proven that CPL-Net can achieve an accuracy of 98.7% and an F1 score of 98.6% for malware. The model uses a novel feature fusion approach and achieves a comprehensive and precise malware detection.