面向电力客户侧终端网络的高效入侵检测模型研究

针对电力客户侧终端网络逐渐开放、设备分散和不易进行安全监测的现状，提出了一种基于LightGBM的高效率网络入侵检测模型。文章在目标编码中引入改进的平滑映射方法，提升了模型的检测效果；利用BPSO算法进行特征选择，设计目标函数，在保障检测准确率的前提下，实现对低价值特征的去除，降低模型的时间开销，并通过设计速度变异策略提升BPSO算法的效率；利用LightGBM算法实现入侵检测和攻击分类，并利用PSO算法实现LightGBM参数的自动选取。基于多个开源数据集的实验表明，所提模型具有较高的自适应能力，在攻击检测上具有较高的准确率、较少的误报和漏报情况，并且可以提升19%的训练和检测效率。

An efficient intrusion detection model for power client side terminal network

Aiming at the current problems of the gradual opening of the power client side terminal network, the scattered equipment and the difficulty of security monitoring, an efficient network intrusion detection model based on LightGBM is proposed. Firstly, this paper introduces an improved smoothing mapping method into the target coding, which improves the detection effect of the model. Secondly, the BPSO algorithm is used for feature selection. By designing the objective function, on the premise of ensuring the detection accuracy, the redundant dimensions are automatically removed and the time overhead of the model is reduced. The efficiency of the BPSO algorithm is improved by designing the speed variation strategy. Finally, the LightGBM algorithm is applied to realize intrusion detection and attack classification, and the PSO algorithm is used to realize the automatic selection of LightGBM parameters. Experiments based on multiple open source datasets show that the proposed model has a high degree of automation, high accuracy in attack detection, less false positives and omissions, and can improve the average detection efficiency by 25%.