在基于区块链 (IoBC) 的能源网络互联网中使用机器学习进行多层网络攻击的识别和分类
Muhammad Faheem1,2,3, Mahmoud Ahmad Al-Khasawneh4
1Department of Computing Science, School of Technology & Innovations, University of Vaasa, Vaasa 65200, Finland.
Data in brief
|May 22, 2024
概括
本研究介绍了一种混合机器学习模型,用于检测智能电网中的拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 等网络攻击. 该模型利用可再生能源系统的大数据来增强网络安全,以确保可靠的能源供应.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络安全 网络安全
背景情况:
- 全球能源需求正在增加,由人口增长和经济扩张推动.
- 化石燃料燃烧为能源加剧了气候变化,并带来了健康风险.
- 通过智能电网 (SG) 过渡到可再生能源引入了新的网络安全挑战.
研究的目的:
- 解决智能电网环境中先进的网络威胁检测和预防的关键需求.
- 开发和应用一种新的混合机器学习 (HML) 模型来识别网络攻击.
- 为了提高安全性,利用基于区块链的可再生能源系统的大数据.
主要方法:
- 从基于区块链的智能电网中的太阳能和风能分布式能源系统收集大数据.
- 开发了一种混合机器学习 (HML) 模型,整合了深度学习 (DL) 和长短期记忆 (LSTM) 的特征.
- 应用HML模型来识别拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 攻击的模式.
主要成果:
- 成功识别出标志着DoS和DDoS网络攻击在发电,输电和配电领域的独特模式.
- 在智能电网基础设施中,HML模型在分类网络攻击方面表现出有效性.
- 生成的大数据集对于准确的能源系统行为预测至关重要.
结论:
- 开发的HML模型为检测和防止智能电网中的网络威胁提供了有效的解决方案.
- 利用可再生能源系统的大数据可以提高智能电网运营的安全性和可靠性.
- 这项研究有助于通过保护智能电网系统免受网络攻击来确保能源的未来.
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