Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Abstract

Considering the robustness and accuracy of conventional intrusion detection models are easily influenced by adversarial attacks, this work proposes an anti-attack intrusion detection algorithm based on a message-passing neural network with traffic spatiotemporal features. Our algorithm can not only effectively distinguish and correlate upstream and downstream traffics, but also clearly embody the relationship between different traffics from the same source node and destination node by the established graph structure. We also improve the model by utilising the characteristics of the existing network attacks, which indicates thatthe traffic spatiotemporal characteristics could achieve high accuracy on attack traffic detection. Compared to the previous algorithms, extensive experiments show that our proposed model outperforms other similar models in performance. For the multi-classification tasks, our algorithm can effectively detect most of the network attacks and outperform traditional machine learning methods. In terms of algorithm robustness, our algorithm is less affected by adversarial attacks than traditional machine learning algorithms.

Research on XSS adversarial attack model based on reinforcement learning

“…Therefore, research on XSS adversarial attack techniques is particularly important. XSS adversarial attack samples can reveal the inherent rules and vulnerabilities of XSS attacks [8] , thereby optimizing detection algorithms and improving their detection performance. Therefore, further improving the accuracy of XSS detection models and providing more valuable XSS attack data samples for XSS attack detection models [9] is currently a major challenge to be addressed.…”