Abnormal traffic detection based on image recognition and attention-residual optimization - Wang, Pengfei; Chen, Xinpeng; Shen, Jinan; Xu, Zhenwu; Liang, Fang; Du, Qiuyang (Frontiers in Communications and Networks, 2025)
With the advancement of Internet of Things (IoT) technology, the continuous growth of IoT systems has resulted in the accumulation of massive amounts of data. Consequently, there has been a sharp increase in network attacks, highlighting the need for enhanced network security methods. Network intrusion detection systems play a crucial role in network security. Compared to the traditional approach of using single time-series models to process traffic data, this study innovatively proposes an RMCLA (Residual Network and Multi-scale Convolution Long Short-Term Memory with Attention Mechanisms) network intrusion detection system optimized with attention and residual mechanisms. This model converts traffic data into feature images and enhances the feature contrast through histogram equalization. It then utilizes the powerful performance of convolutional networks to extract abnormal feature points. The attention module and residual network enhance the focus on abnormal points, reducing feature loss and redundancy, thereby achieving effective classification of traffic image processing. We conducted experiments on the CIC-IDS2017 and UNSW-NB15 datasets and compared our model with the latest research models. This study highlights the potential of combining deep learning techniques with advanced attention and residual networks to enhance network security in IoT environments. The results show that combining image recognition with attention-residual optimization can effectively improve network intrusion detection capabilities.