Edge computing, as a new computing model, is facing new challenges in network security while developing rapidly. Due to the limited performance of edge nodes, the distributed intrusion detection system (DIDS), which relies on high-performance devices in cloud computing, needs to be improved to low load to detect packets nearby the network edge. This paper proposes a low load DIDS task scheduling method based on Q-Learning algorithm in reinforcement learning, which can dynamically adjust scheduling strategies according to network changes in the edge computing environment to keep the overall load of DIDS at a low level, while maintaining a balance between the two contradictory indicators of low load and packet loss rate. Simulation experiments show that the proposed method has better low-load performance than other scheduling methods, and indicators such as malicious feature detection rate are not significantly reduced.

边缘计算作为一种新的计算模式，在快速发展的同时也面临着网络安全方面的新挑战。由于边缘节点的性能有限，云计算中依赖高性能设备的分布式入侵检测系统（DIDS）需要改进为低负载以检测网络边缘附近的数据包。本文在强化学习中提出了一种基于Q-Learning算法的低负载DIDS任务调度方法，可以根据边缘计算环境中的网络变化动态调整调度策略，使DIDS的整体负载保持在较低水平，同时保持平衡低负载和丢包率这两个矛盾的指标。仿真实验表明，与其他调度方法相比，该方法具有更好的低负载性能，