Alhazmi Mohannad, Zhao Alexis Pengfei, Cheng Xi, Yang Chenlu
Electrical Engineering Department, College of Applied Engineering, King Saud University, P.O. Box 2454, 11451, Riyadh, Saudi Arabia.
Department of Energy Science and Engineering, Stanford Doerr School of Sustainability, Stanford University, Stanford, CA, 94305, USA.
Sci Rep. 2025 Jul 11;15(1):25051. doi: 10.1038/s41598-025-10582-1.
The increasing integration of digital technologies in hybrid hydrogen-power networks has introduced new cybersecurity vulnerabilities that existing static or single-phase cyberattack models fail to adequately exploit or defend against. These models typically lack dynamic adaptability, coordination across multiple attack stages, and obfuscation mechanisms, thereby limiting their effectiveness and realism. To address this gap, we propose a novel Cyberattack Design Based on CNN-DQN-Blockchain Technology for Targeted Adaptive Strategy (CDB-TAS)-a three-stage, dynamically evolving cyberattack framework tailored for hybrid hydrogen-electric networks. The proposed CDB-TAS model comprises: (i) a Preliminary Reconnaissance Phase, where a Convolutional Neural Network (CNN) identifies the most vulnerable buses via real-time anomaly detection; (ii) an Escalation Phase, where a Double Deep Q-Network (Double DQN) dynamically refines the attack strategy based on grid response and demand profiles; and (iii) a Sustained Attack Phase, which maintains high-intensity disruptions while minimizing detection through continuous feedback adaptation. Additionally, a private blockchain network is employed not for defense, but as an attacker-side obfuscation layer-concealing attack metadata and enabling decentralized coordination among malicious nodes. Simulations on a synthetic 2000-bus hybrid hydrogen-power system modeled after ERCOT reveal that CDB-TAS induces up to 15% voltage drop at critical buses (e.g., Bus 3103), disrupts over 600 MW of load across 50 substations, and achieves 23.4% higher disruption efficiency with lower anomaly detection rates compared to baseline attacks. This study presents the first integrated framework combining CNN, reinforcement learning, and blockchain from an adversarial perspective, offering new insights into the evolving threat landscape and guiding the development of future cyber-resilience strategies in multi-energy systems.
数字技术在混合氢能源网络中的日益融合带来了新的网络安全漏洞，而现有的静态或单相网络攻击模型无法充分利用或抵御这些漏洞。这些模型通常缺乏动态适应性、跨多个攻击阶段的协调能力以及混淆机制，从而限制了它们的有效性和现实性。为了弥补这一差距，我们提出了一种基于CNN-DQN-区块链技术的新型目标自适应策略网络攻击设计（CDB-TAS）——一种为混合氢电网络量身定制的三阶段动态演进网络攻击框架。所提出的CDB-TAS模型包括：（i）初步侦察阶段，其中卷积神经网络（CNN）通过实时异常检测识别最脆弱的母线；（ii）升级阶段，其中双深度Q网络（Double DQN）根据电网响应和需求曲线动态优化攻击策略；（iii）持续攻击阶段，该阶段保持高强度干扰，同时通过持续反馈自适应将检测降至最低。此外，使用私有区块链网络并非用于防御，而是作为攻击者端的混淆层——隐藏攻击元数据并实现恶意节点之间的分散协调。在以ERCOT为模型的合成2000母线混合氢能源系统上进行的模拟表明，与基线攻击相比，CDB-TAS在关键母线（如母线3103）处可导致高达15%的电压降，扰乱50个变电站超过600兆瓦的负荷，并以更低的异常检测率实现高23.4%的干扰效率。本研究从对抗的角度提出了第一个结合CNN、强化学习和区块链的综合框架，为不断演变的威胁格局提供了新的见解，并指导多能源系统中未来网络弹性策略的发展。
