Leveraging Deep Learning to Strengthen the Cyber-Resilience of Renewable Energy Supply Chains: A Survey
MN Halgamuge - IEEE Communications Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Deep learning shows immense potential for strengthening the cyber-resilience of renewable
energy supply chains. However, research gaps in comprehensive benchmarks, real-world …
energy supply chains. However, research gaps in comprehensive benchmarks, real-world …
CySCPro-Cyber Supply Chain Provenance Framework for Risk Management of Energy Delivery Systems
For operational efficiency, enterprise-level Energy Delivery Systems (EDS) rely on a number
of software or hardware providers. Overseas suppliers generally manufacture and integrate …
of software or hardware providers. Overseas suppliers generally manufacture and integrate …
DFF-SC4N: A deep federated defence framework for protecting supply chain 4.0 networks
The management of contemporary communication networks of supply chain (SC) 4.0 is
becoming more complex due to the heterogeneity requirements of new devices concerning …
becoming more complex due to the heterogeneity requirements of new devices concerning …
Cyber resilience framework for online retail using explainable deep learning approaches and blockchain-based consensus protocol
Online retail platforms encounter numerous challenges, such as cyber-attacks, data
breaches, device failures, and operational disruptions. These challenges have intensified in …
breaches, device failures, and operational disruptions. These challenges have intensified in …
An efficient evolutionary deep learning-based attack prediction in supply chain management systems
Abstract Supply Chain Management Systems (SCM) is the critical infrastructure that can be
treated as a significant factor since it forms advancement in intelligent devices. The cyber …
treated as a significant factor since it forms advancement in intelligent devices. The cyber …
Deep learning for cybersecurity in smart grids: Review and perspectives
Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered
significant attention in recent years. The application of artificial intelligence (AI), particularly …
significant attention in recent years. The application of artificial intelligence (AI), particularly …
The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey
N Abdi, A Albaseer, M Abdallah - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
As smart grids (SGs) increasingly rely on advanced technologies like sensors and
communication systems for efficient energy generation, distribution, and consumption, they …
communication systems for efficient energy generation, distribution, and consumption, they …
Data security of machine learning applied in low-carbon smart grid: A formal model for the physics-constrained robustness
Towards the low-carbon goal, a smart grid features remote connection, data sharing, and
cyber–physical integration to increase the flexibility of energy supplies, to reduce electricity …
cyber–physical integration to increase the flexibility of energy supplies, to reduce electricity …
DeepFed: Federated deep learning for intrusion detection in industrial cyber–physical systems
The rapid convergence of legacy industrial infrastructures with intelligent networking and
computing technologies (eg, 5G, software-defined networking, and artificial intelligence) …
computing technologies (eg, 5G, software-defined networking, and artificial intelligence) …
Resilience of cyber-physical systems: Role of AI, digital twins, and edge computing
AS Jin, L Hogewood, S Fries… - IEEE Engineering …, 2022 - ieeexplore.ieee.org
Cyber-physical systems encompass multiple system domains (ie, water, energy, networking)
with heterogeneous goals and complexity of interactions. Existing technologies do not …
with heterogeneous goals and complexity of interactions. Existing technologies do not …