Deep learning for cybersecurity in smart grids: Review and perspectives

[1]  Zhao Yang Dong,et al.  Super-Resolution Perception Assisted Spatiotemporal Graph Deep Learning Against False Data Injection Attacks in Smart Grid , 2023, IEEE Transactions on Smart Grid.

[2]  F. Moazeni,et al.  Neural Networks-Based Detection of Line Overflow Cyberattacks on AC State-Estimation of Smart Grids , 2023, IEEE Systems Journal.

[3]  T. K. Saha,et al.  Deep Latent Space Clustering for Detection of Stealthy False Data Injection Attacks Against AC State Estimation in Power Systems , 2023, IEEE Transactions on Smart Grid.

[4]  Ruilong Deng,et al.  Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF , 2023, IEEE Transactions on Power Systems.

[5]  S. Mekhilef,et al.  Detection of false data injection attacks in cyber–physical systems using graph convolutional network , 2023, Electric Power Systems Research.

[6]  Tingwen Huang,et al.  Differential Evolution-Based Three Stage Dynamic Cyber-Attack of Cyber-Physical Power Systems , 2023, IEEE/ASME Transactions on Mechatronics.

[7]  Yinghua Han,et al.  False data injection attacks detection with modified temporal multi-graph convolutional network in smart grids , 2023, Comput. Secur..

[8]  Mohamed I. Ibrahem,et al.  Electricity-Theft Detection for Change-and-Transmit Advanced Metering Infrastructure , 2022, IEEE Internet of Things Journal.

[9]  Ümmühan Başaran Filik,et al.  A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid , 2022, Int. J. Crit. Infrastructure Prot..

[10]  Charalambos Konstantinou,et al.  Electric load forecasting under False Data Injection Attacks using deep learning , 2022, Energy Reports.

[11]  A. Abdelaziz,et al.  Deep learning-based identification of false data injection attacks on modern smart grids , 2022, Energy Reports.

[12]  Xueping Li,et al.  Graph-based detection for false data injection attacks in power grid , 2022, Energy.

[13]  A. Jolfaei,et al.  Experience-Driven Attack Design and Federated-Learning-Based Intrusion Detection in Industry 4.0 , 2022, IEEE Transactions on Industrial Informatics.

[14]  Ali Jafarian Abianeh,et al.  Vulnerability Identification and Remediation of FDI Attacks in Islanded DC Microgrids Using Multiagent Reinforcement Learning , 2022, IEEE Transactions on Power Electronics.

[15]  G. Hancke,et al.  Resilient Optimal Defensive Strategy of Micro-Grids System via Distributed Deep Reinforcement Learning Approach Against FDI Attack. , 2022, IEEE transactions on neural networks and learning systems.

[16]  F. Moazeni,et al.  Detection of false data injection attacks leading to line congestions using Neural Networks , 2022, Sustainable Cities and Society.

[17]  Debottam Mukherjee.  A novel strategy for locational detection of false data injection attack , 2022, Sustainable Energy, Grids and Networks.

[18]  Seyed Hossein Majidi,et al.  FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid , 2022, Int. J. Crit. Infrastructure Prot..

[19]  F. Wen,et al.  A survey on policies, modelling and security of cyber‐physical systems in smart grids , 2021, Energy Conversion and Economics.

[20]  Liqun Yang,et al.  Deep learning for online AC False Data Injection Attack detection in smart grids: An approach using LSTM-Autoencoder , 2021, J. Netw. Comput. Appl..

[21]  Jingbo Hao,et al.  Adversarial attacks on deep learning models in smart grids , 2021, Energy Reports.

[22]  Charalambos Konstantinou,et al.  Cyber-Resilient Smart Cities: Detection of Malicious Attacks in Smart Grids , 2021, Sustainable Cities and Society.

[23]  Youyang Qu,et al.  A Covert Electricity-Theft Cyberattack Against Machine Learning-Based Detection Models , 2021, IEEE Transactions on Industrial Informatics.

[24]  Mohammad Ashiqur Rahman,et al.  Resiliency-Aware Deployment of SDN in Smart Grid SCADA: A Formal Synthesis Model , 2021, IEEE Transactions on Network and Service Management.

[25]  A. Ketabi,et al.  Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios , 2021, Sustainability.

[26]  Z. Dong,et al.  Spot electricity market design for a power system characterized by high penetration of renewable energy generation , 2021, Energy Conversion and Economics.

[27]  Dimitrios Tzovaras,et al.  Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm , 2021, Comput. Sci. Rev..

[28]  Erchin Serpedin,et al.  Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids , 2021, IEEE Transactions on Smart Grid.

[29]  Zhaopeng Tu,et al.  On the diversity of multi-head attention , 2021, Neurocomputing.

[30]  Isabel Praça,et al.  Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities , 2021, Artificial Intelligence Review.

[31]  Haibo He,et al.  Coordinated Topology Attacks in Smart Grid Using Deep Reinforcement Learning , 2021, IEEE Transactions on Industrial Informatics.

[32]  Erchin Serpedin,et al.  A3D: Attention-based auto-encoder anomaly detector for false data injection attacks , 2020, Electric Power Systems Research.

[33]  Gang Xie,et al.  Detecting false data attacks using machine learning techniques in smart grid: A survey , 2020, J. Netw. Comput. Appl..

[34]  Janusz Bialek,et al.  What does the GB power outage on 9 August 2019 tell us about the current state of decarbonised power systems? , 2020 .

[35]  Liang Zhao,et al.  DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems , 2020, IEEE Transactions on Industrial Informatics.

[36]  Namje Park,et al.  Blockchain-Based Data-Preserving AI Learning Environment Model for AI Cybersecurity Systems in IoT Service Environments , 2020, Applied Sciences.

[37]  Pedro Cruz-Romero,et al.  Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters , 2020, IEEE Transactions on Power Systems.

[38]  Yuancheng Li,et al.  Efficient detection of false data injection attack with invertible automatic encoder and long-short-term memory , 2019, IET Cyper-Phys. Syst.: Theory & Appl..

[39]  Hanghang Tong,et al.  Graph convolutional networks: a comprehensive review , 2019, Computational Social Networks.

[40]  Chong Zhou,et al.  Modern Machine Learning for Cyber-Defense and Distributed Denial-of-Service Attacks , 2019, IEEE Engineering Management Review.

[41]  Jinkuan Wang,et al.  Electricity Theft Detection in Power Grids with Deep Learning and Random Forests , 2019, J. Electr. Comput. Eng..

[42]  Bin Zhou,et al.  Deep learning aided interval state prediction for improving cyber security in energy internet , 2019, Energy.

[43]  Baosen Zhang,et al.  Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks , 2019, e-Energy.

[44]  Naima Kaabouch,et al.  Cyber-security in smart grid: Survey and challenges , 2018, Comput. Electr. Eng..

[45]  Paul Barford,et al.  Data Poisoning Attacks against Autoregressive Models , 2016, AAAI.

[46]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[47]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[48]  Aaron C. Courville,et al.  Generative adversarial networks , 2014, Commun. ACM.

[49]  M. Reiter,et al.  False data injection attacks against state estimation in electric power grids , 2011, TSEC.

[50]  Geoffrey E. Hinton.  Deep belief networks , 2009, Scholarpedia.

[51]  Craig Gentry,et al.  Fully homomorphic encryption using ideal lattices , 2009, STOC '09.

[52]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[53]  Nicolas Le Roux,et al.  Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.

[54]  Wenliang Du,et al.  Secure multi-party computation problems and their applications: a review and open problems , 2001, NSPW '01.

[55]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[56]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[57]  Rong Huang,et al.  Attention-aware deep reinforcement learning for detecting false data injection attacks in smart grids , 2023, International Journal of Electrical Power & Energy Systems.

[58]  Zhao Yang Dong,et al.  Graph Deep Learning-based Retail Dynamic Pricing for Demand Response , 2023, IEEE Transactions on Smart Grid.

[59]  Zhengwei Qu,et al.  False data injection attack detection based on interval affine state estimation , 2022, Electric Power Systems Research.

[60]  Xiaoge Huang,et al.  Defense of Massive False Data Injection Attack via Sparse Attack Points Considering Uncertain Topological Changes , 2022, Journal of Modern Power Systems and Clean Energy.

[61]  Yaze Li,et al.  Low Latency Cyberattack Detection in Smart Grids with Deep Reinforcement Learning , 2022, SSRN Electronic Journal.

[62]  Mohamed I. Ibrahem,et al.  Real-time Detection of False Readings in Smart Grid AMI using Deep and Ensemble Learning , 2022, IEEE Access.

[63]  Song Tan,et al.  Survey of Security Advances in Smart Grid: A Data Driven Approach , 2017, IEEE Communications Surveys & Tutorials.

[64]  Geoffrey E. Hinton,et al.  Melting of Peridotite to 140 Gigapascals , 2010, Science.

[65]  Marco Tulio Ribeiro,et al.  “Why Should I Trust You?”: Explaining the Predictions of Any Classifier , 2016, NAACL.