基于混合深度学习的合并模型用于智能电网网络中的入侵检测
Ulaa AlHaddad1, Abdullah Basuhail1, Maher Khemakhem1
1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了一种混合深度学习模型,用于检测智能电网通信网络上的网络攻击. 这种新的方法实现了99.86%的准确性,增强了对分布式拒绝服务威胁的网络安全和可靠性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 智能电网系统通过数字技术提高电网效率和可靠性.
- 通信网络至关重要,但引入网络攻击的脆弱性,危及电网稳定性.
- 侵入检测和预防对于减轻这些网络威胁至关重要.
研究的目的:
- 提出一种混合深度学习方法来检测分布式拒绝服务 (DDoS) 攻击.
- 提高智能电网通信基础设施的安全性和弹性.
- 开发用于攻击监控的实时监控系统.
主要方法:
- 一种混合深度学习模型,结合了卷积神经网络 (CNN) 和循环门单元 (GRU) 算法.
- 使用了两个数据集:加拿大网络安全研究所的入侵检测系统数据集和自定义的Omnet++模拟数据集.
- 开发了一个基于卡夫卡的仪表板,用于实时监控和攻击监视.
主要成果:
- 拟议的混合深度学习模型实现了99.86%的高检测精度.
- 在模拟和现实数据集中有效检测分布式拒绝服务攻击.
- 实时监控仪表板促进了有效的攻击监控.
结论:
- 混合深度学习方法在检测智能电网通信网络中的网络攻击方面非常有效.
- 这种方法显著提高了智能电网的安全性和可靠性.
- 开发的系统为实时威胁检测和缓解提供了强大的解决方案.
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