A Data-Driven Approach to Distinguish Cyber-Attacks from Physical Faults in a Smart Grid
Author:
Affiliation:
1. UNSW Australia, Canberra, Australia
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2806416.2806648
Reference19 articles.
1. Power System State Estimation
2. A cyber-physical power system test bed for intrusion detection systems
3. Vulnerabilities of Smart Grid State Estimation Against False Data Injection Attack
4. Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid
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2. Nonlinear Controller-Based Mitigation of Adverse Effects of Cyber-Attacks on the DC Microgrid System;Electronics;2024-03-12
3. Misbehaviour Detection for Smart Grids using a Privacy-centric and Computationally Efficient Federated Learning Approach;GLOBECOM 2023 - 2023 IEEE Global Communications Conference;2023-12-04
4. Diagnosing Faults in Smart Grids using Liquid Time-Constant Network;2023 International Conference on Engineering and Emerging Technologies (ICEET);2023-10-27
5. FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2023-05-20
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