基于卷积神经网络的电网工控系统入侵检测算法

传统的电网工控系统主要通过防火墙等工具, 与外部网络进行隔离, 但是随着云计算、物联网等新技术的应用, 网络之间互联程度不断深入, 安全防护难度大大提高, 如何有效检测出网络入侵行为变得至关重要. 与传统入侵检测技术相比, 卷积神经网络具有更好的提取入侵特征的能力. 本文提出一种基于卷积神经网络的电网工控系统入侵检测算法, 使用经过处理的KDD99数据集进行模型训练, 并添加级联卷积层优化网络结构. 在参数规模不大的前提下, 保证了模型运行的实时性要求. 本文算法相对于传统SVM算法和K-means算法, 提高了入侵检测的准确率, 降低了误检率, 可以有效检测出对于电网工控系统的入侵行为.

Intrusion Detection Algorithm of Power Grid Industrial Control System Based on CNN

Traditional power grid industrial control systems are mainly isolated from external networks through tools such as firewalls, but with the application of new technologies such as cloud computing and the Internet of Things, the degree of interconnection between networks has continued to deepen, and the difficulty of security protection has greatly increased. How to effectively detect network intrusion behavior has become very important. Compared with traditional intrusion detection technology, convolutional neural networks have a better ability to extract intrusion features. This study proposes a power grid industrial control system intrusion detection algorithm based on convolutional neural networks. The KDD99 dataset is processed for model training, and a cascade convolution layer is added to optimize the network structure. Under the premise of small parameter scale, the real-time requirements of the model are guaranteed. Compared with the traditional SVM algorithm and the k-means algorithm, the intrusion detection accuracy of the proposed algorithm in this study is improved, the false detection rate is reduced, and the intrusion behavior to the power grid industrial control system can be effectively detected.