FC-Trans: Deep learning methods for network intrusion detection in big data environments

Abstract

With the continuous expansion of Internet traffic, effectively preventing network intrusions in such a vast data environment has become increasingly challenging. Existing intrusion detection systems (IDS) for different network attacks often struggle to identify unknown attacks or respond to them in real-time. In this article, we propose a novel hybrid deep learning model, FC-Trans, designed to enhance network intrusion monitoring. Our approach involves optimizing feature representation using the Feature Tokenizer method, leveraging CNNs to extract meaningful features from the data, and incorporating Transformer’s self-attentive mechanism and residual structure to capture long-term feature dependencies and mitigate gradient vanishing. To address the issue of imbalanced sample distribution, we utilize MultiF Loss as the training loss function for the multi-classification task, enabling the model to prioritize difficult-to-classify samples. We compare the performance of our method with other approaches on the UNSW-NB15 dataset, and the experimental results demonstrate significant improvements in both binary and multivariate classification tasks. The results verify the effectiveness of our proposed method.