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In response to escalating cybersecurity threats, this study aims to develop a lightweight deep learning model for efficient and accurate detection and classification of malicious code. To achieve this goal, the study introduces TriCh-RepNet, a novel network architecture that balances performance and resource utilization. The methodology involves innovatively transforming malicious code representations into image channels through a three-channel mapping technique, thereby enhancing information richness and discriminatory power. By integrating the strengths of Convolutional Neural Networks (CNNs) and Transformers, TriCh-RepNet creates a streamlined framework that optimizes network connections, reduces memory access overhead, and boosts overall efficiency. Additionally, the combination of linear training time over-parameterization and large kernel convolution techniques minimizes the model's parameter count and computational load. The model achieves accuracy rates of 99.47% and 97.51% 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, especially in resource-constrained or real-time environments. A novel and effective solution for malicious code detection and categorization.
Li et al. (Mon,) studied this question.