A Deep Learning Based Intrusion Detection System for Electric Distribution Grids

LIU Wen-jun; GUO Zhi-min; WU Chun-ming; RUAN Wei; ZHOU Bo-yang; ZHOU Ning; L

doi:10.3969/j.issn.0372-2112.2020.08.011

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A Deep Learning Based Intrusion Detection System for Electric Distribution Grids

更新时间：2025-07-16

- A Deep Learning Based Intrusion Detection System for Electric Distribution Grids
- Acta Electronica Sinica Vol. 48, Issue 8, Pages: 1538-1544(2020)
- 作者机构：
  
  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
  5. 国网河南省电力公司,河南,郑州,450000
  6. 国网河南省电力公司电力科学研究院,河南,郑州,450000
  7. 浙江大学计算机科学与技术学院,浙江,杭州,310027
  8. 浙江大学控制科学与工程学院,浙江,杭州,310027
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.08.011
  CLC： TP393
- Published Online：25 August 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
LIU Wen-jun, GUO Zhi-min, WU Chun-ming, et al. A Deep Learning Based Intrusion Detection System for Electric Distribution Grids[J]. Acta Electronica Sinica, 2020, 48(8): 1538-1544.
DOI：

LIU Wen-jun, GUO Zhi-min, WU Chun-ming, et al. A Deep Learning Based Intrusion Detection System for Electric Distribution Grids[J]. Acta Electronica Sinica, 2020, 48(8): 1538-1544. DOI： 10.3969/j.issn.0372-2112.2020.08.011.

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%.

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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