TriCh-LKRepNet: A large kernel convolutional malicious code classification network for structure reparameterisation and triple-channel mapping

Abstract

In response to escalating cybersecurity threats, this study aims to develop a lightweight deep learning model for efficient and accurate detection and classification of malware. To achieve this goal, the study introduces RGB-MalNet, a novel network architecture that balances performance and resource utilization. The method innovatively transforms malware representations into image channels through RGB three-channel mapping, which improves information richness and discriminative power. RGB-MalNet creates a streamlined framework that optimizes network connections, reduces memory access overhead, and boosts overall efficiency. The model achieves accuracy rates of 99.47% and 97.55% on the Kaggle and DataCon datasets, respectively. Compared to existing methodologies, this approach stands out in terms of performance, resource consumption, and versatility. It offers a viable and efficient solution for malicious code detection and classification.