Special issue on Security and Privacy in Smart Grid and Machine Learning
摘要截稿:
全文截稿: 2020-06-01
影响因子: 5.268
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 结构与建筑技术 - 2区
• 小类 : 能源与燃料 - 2区
• 小类 : 绿色可持续发展技术 - 2区
Overview
Smart Grid is the next generation of the electrical grid, which is envisioned to revolutionize the way electricity is generated, distributed and monitored. It is strongly believed that it will make the life of the next generations and us a lot safer and sustainable. Therefore, many countries have already taken major steps towards its adoption to gain these benefits. However, there are several issues, which need to be addressed before this dream, can be fully realized. Among the most pressing issues security and privacy is the most serious. The smart grid is exposed to a wide array of threats including data theft, false data injection, denial of service attacks, data privacy, insider attacks, malware attacks, DDoS attacks, energy theft, etc. On the other hand, advancements in cryptography, differential privacy and secure multi-party computation have promised a lot. However, there is still much to be desired from these approaches. The integration of the cloud-fog based computing model has also provided great prospects in forwarding towards the desired goals of Smart Grid. However, we are still far behind achieving the desired goals.
Machine-learning based approaches have been also deployed to address the cyber security issues in various domains. However, the cutting-edge deep learning-based approaches have not been studied for addressing the security and privacy problems in the smart grids. Due to the critical nature of the smart grid operations, it is imperative to study deep learning based models in addressing these issues. Thus, this special issue will focus on addressing the security and privacy issues of the smart grid in the context of machine learning or deep learning-based models. Submissions could consist of novel ideas, original results, theoretical and applied research in the following topics, but not limited to:
Trust, Privacy, management issues and their countermeasures,
Economics and performance analysis of smart grid using deep learning models
Cybersecurity management in smart grid and their implications using deep neural network models
Deep learning empowered Malware analysis techniques for smart grid and industrial IoT
Advanced cryptographic technique supporting privacy preservation in smart grid communications
Benchmarking machine learning models for smart grid communications
Big data, IoT and machine learning for resilient smart grid infrastructure
Integration of secure solutions for industrial internet-of-things and internet of energy
Security, interoperability and design models for smart grids using deep learning models
Robustness, fault-tolerance in smart grid using deep learning models
Privacy preserving data aggregation and protection using deep learning models
Privacy preserving using fully homomorphic encryption schemes in smart grids.
Differential privacy and deep learning for smart grid communication.
Fault prediction, diagnosis and avoidance using deep learning models.
Deep learning empowered forensics techniques for smart grid.
Machine learning and deep learning for resilient and efficient smart grid working.
Intelligent data collection and inspection models using deep learning
Security and Privacy issues in Fog/edge-enabled model for smart grid