Network Intrusion Detection Adversarial Attacks for LEO Constellation Networks Based on Deep Learning

Low-Earth orbit satellite networks have received attention from academia and industry for their advantages in terms of wide coverage and low latency. Meantime deep learning can provide more accurate traffic anomaly detection and has become an important class of methods for LEO satellite network security. However, deep learning is susceptible to adversarial sample attacks, and the LEO satellite network system has not been investigated to find a framework for adversarial sample attacks and defence systems, which poses a potential risk to network communication security. In this paper, we design a framework to generate and defend against adversarial samples in real time. By capturing traffic from LEO satellites, it can generate traffic adversarial samples to detect whether malicious traffic classification models are vulnerable to attacks, and defense against adversarial sample attacks in real time. In this paper, a simple LEO satellite simulation platform is built to generate traffic adversarial samples using four classical adversarial sample generation methods, and a two-classification deep learning model is trained to determine the effectiveness of the attack and defence. Experiments show that the framework proposed in the paper can crawl traffic and perform self-attack and defence tests.