基于深度学习的配电网无线通信入侵检测系统

A Deep Learning Based Intrusion Detection System for Electric Distribution Grids

在采用无线通信接入的配电网中,入侵检测系统(IDS)通过分析通信网中传输数据来判断入侵事件.为提高检测的准确性,本文将深度学习理论应用于IDS,提出了一种面向配电网无线通信网络新型入侵检测系统,由带有门控循环单元、多层感知器和Softmax的循环神经网络组成.攻击测试基准实验结果表明IDS防御的有效性,在KDD99测试数据集上,其误报率为0.06%,总检出率为96.43%;在NSL-KDD测试数据集上,其误报率低至0.86%,总检出率则为99.33%.

In an electric power distribution grid using wireless communication access,IDS is used to decide system the intrusive event through analyzing the network transmission data.In this paper,to improve the detection accuracy,a deep learning theory is studied for the IDS in the wireless communication network of a power distribution grid.The proposed Recurrent Neural Network(RNN)model is composed of Gated Recurrent Unit(GRU),Multi-Layer Perceptron(MLP)and Softmax.The experimental results on the attack testing baseline demonstrate the effectiveness of the IDS defenses.In the KDD99 test data,its negative error rate and accuracy are with 0.06%and 96.43%,and in the NSL-KDD test data,those statistics are 0.86%with 99.33%,respectively.