| 基于深度学习的配电网无线通信入侵检测系统 |
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| 引用本文： | 刘文军,郭志民,吴春明,阮伟,周伯阳,周宁,吕卓.基于深度学习的配电网无线通信入侵检测系统[J].电子学报,2020,48(8):1538-1544. |
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| 作者姓名： | 刘文军 郭志民 吴春明 阮伟 周伯阳 周宁 吕卓 |
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| 作者单位： | 1. 国网河南省电力公司, 河南郑州 450000;
2. 国网河南省电力公司电力科学研究院, 河南郑州 450000;
3. 浙江大学计算机科学与技术学院, 浙江杭州 310027;
4. 浙江大学控制科学与工程学院, 浙江杭州 310027 |
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| 摘 要： | 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%.
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| 关 键 词： | 配电网 无线网 入侵检测 深度学习 递归神经网络 |
| 收稿时间： | 2018-04-18 |
A Deep Learning Based Intrusion Detection System for Electric Distribution Grids |
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| LIU Wen-jun,GUO Zhi-min,WU Chun-ming,RUAN Wei,ZHOU Bo-yang,ZHOU Ning,Lü Zhuo.A Deep Learning Based Intrusion Detection System for Electric Distribution Grids[J].Acta Electronica Sinica,2020,48(8):1538-1544. |
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| Authors: | LIU Wen-jun GUO Zhi-min WU Chun-ming RUAN Wei ZHOU Bo-yang ZHOU Ning Lü Zhuo |
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| Institution: | 1. State Grid Henan Electric Power Company, Zhengzhou, Henan 450000, China;
2. State Grid Henan Electric Power Research Institute, State Grid Henan Electric Power Company, Zhengzhou, Henan 450000, China;
3. College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China;
4. College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China |
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| Abstract: | 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. |
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| Keywords: | electric distribution network wireless network intrusion detection deep learning recurrent neural network(RNN) |
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