在电力系统中进行多阶段适应性网络攻击,采用CNN识别反循环.
Mohannad Alhazmi1, Alexis Pengfei Zhao2, Xi Cheng3
1Electrical Engineering Department, College of Applied Engineering, King Saud University, P.O. Box 2454, 11451, Riyadh, Saudi Arabia. Mohalhazmi@ksu.edu.sa.
Scientific reports
|July 11, 2025
概括
一种新的网络攻击模式,CDB-TAS,使用CNN,双DQN和区块链对混合动力网络进行动态,自适应性攻击,提高干扰效率,同时逃避检测.
科学领域:
- 网络安全 网络安全
- 能源系统工程 能源系统工程
- 人工智能的人工智能
背景情况:
- 混合动力网络的数字化整合创造了新的网络安全漏洞.
- 现有的网络攻击模型是静态的,缺乏动态的适应性,阻碍了现实的防御策略.
研究的目的:
- 为混合电网络提出一个新的,三阶段的,动态发展的网络攻击框架 (CDB-TAS).
- 通过结合动态适应,多阶段协调和掩盖来提高网络攻击的真实性和有效性.
主要方法:
- 开发了一个基于CNN-DQN-Blockchain技术的针对性自适应战略 (CDB-TAS) 的网络攻击设计.
- 利用卷积神经网络 (CNN) 进行侦察和异常检测.
- 使用双深Q网络 (双DQN) 进行动态策略改进.
- 集成了一个私有区块链,用于攻击者侧的模糊和分散的协调.
主要成果:
- CDB-TAS在关键总线引起了高达15%的电压下降,并破坏了超过600兆瓦的负载.
- 与基线攻击相比,实现了23.4%的更高破坏效率.
- 由于持续反适应和模糊化,证明了较低的异常检测率.
结论:
- 该研究提出了第一个综合框架,从对抗的角度结合了CNN,强化学习和区块链.
- CDB-TAS提供了对混合动力系统不断变化的威胁格局的新见解.
- 调查结果指导了多能源系统未来网络弹性战略的制定.
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