Deep learning shows immense potential for strengthening the cyber-resilience of renewable energy supply chains. However, research gaps in comprehensive benchmarks, real-world model evaluations, and data generation tailored to the renewable domain persist. This study explores applying state-of-the-art deep learning techniques to secure renewable supply chains, drawing insights from over 300 publications. We aim to provide an updated, rigorous analysis of deep learning applications in this field to guide future research. We systematically review literature spanning 2020–2023, retrieving relevant articles from major databases. We examine deep learning’s role in intrusion/anomaly detection, supply chain cyberattack detection frameworks, security standards, historical attack analysis, data management strategies, model architectures, and supply chain cyber datasets. Our analysis demonstrates deep learning enables renewable supply chain anomaly detection by processing massively distributed data. We highlight crucial model design factors, including accuracy, adaptation capability, communication security, and resilience to adversarial threats. Comparing 18 major historical attacks informs risk analysis. We also showcase potential deep learning architectures, evaluating their relative strengths and limitations in security applications. Moreover, our review emphasizes best practices for renewable data curation, considering quality, labeling, access efficiency, and governance. Effective deep learning integration necessitates tailored benchmarks, model tuning guidance, and renewable energy data generation. Our multi-dimensional analysis motivates focused efforts on enhancing detection explanations, securing communications, continually retraining models, and establishing standardized assessment protocols. Overall, we provide a comprehensive roadmap to progress renewable supply chain cyber-resilience leveraging deep learning’s immense potential.

中文翻译：

---

利用深度学习增强可再生能源供应链的网络弹性：一项调查


深度学习显示出增强可再生能源供应链网络弹性的巨大潜力。然而，在综合基准、现实世界模型评估和针对可再生能源领域定制的数据生成方面的研究差距仍然存在。这项研究探索了应用最先进的深度学习技术来确保可再生供应链的安全，从 300 多篇出版物中汲取了见解。我们的目标是对该领域的深度学习应用提供最新、严格的分析，以指导未来的研究。我们系统地回顾了 2020-2023 年的文献，从主要数据库中检索相关文章。我们研究了深度学习在入侵/异常检测、供应链网络攻击检测框架、安全标准、历史攻击分析、数据管理策略、模型架构和供应链网络数据集中的作用。我们的分析表明，深度学习可以通过处理大规模分布式数据来实现可再生供应链异常检测。我们强调关键的模型设计因素，包括准确性、适应能力、通信安全和对抗威胁的弹性。比较 18 种主要的历史攻击可为风险分析提供参考。我们还展示了潜在的深度学习架构，评估它们在安全应用中的相对优势和局限性。此外，我们的审查强调可再生数据管理的最佳实践，考虑质量、标签、访问效率和治理。有效的深度学习集成需要量身定制的基准、模型调整指导和可再生能源数据生成。 我们的多维分析促使我们集中精力加强检测解释、确保通信安全、不断重新训练模型和建立标准化评估协议。总的来说，我们提供了一个全面的路线图，以利用深度学习的巨大潜力来提高可再生供应链的网络弹性。