P4-INT-Based Network Intrusion Detection Method Using CNN-BiLSTM with Multi-Head Attention

Yingying Shao, Shuaishuai Wen, Chengjie Sun

Abstract: With the widespread use of Internet services, the risk of cyber attacks has increased significantly. Existing anomaly-based network intrusion detection systems suffer from slow processing speeds and low detection accuracy due to limitations in datasets and algorithms. Consequently, there is a pressing need to develop high-performance, flexible network intrusion detection systems. In response to these limitations, this paper designs and implements a network intrusion detection system based on P4-INT. This system uses the programmable packet processing language P4 to execute in-band network telemetry, enabling the real-time collection of network data. Additionally, a hybrid network model combining CNN, BiLSTM, and Multi-Head Attention mechanisms is developed to enhance detection capabilities. The proposed solution integrates telemetry technology with deep learning algorithms, effectively addressing the shortcomings of existing systems by improving detection accuracy. Compared to conventional network intrusion detection systems, this system can not only identify new attack patterns but also provide more precise detection results. It holds significant practical value and offers broad application prospects.

External IDs:dblp:conf/cscwd/ShaoWS25