Machine Learning for Cybersecurity Protection of Power Grid Control Infrastructures
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".
Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 2269
Special Issue Editors
Interests: fault detection and diagnosis; failure prognosis; cyberattack detection; fault-resilient control; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis; prognostics and health management; predictive maintenance; condition monitoring; cybersecurity; machine learning
Interests: electric machines and drives; tidal and wave power; wind power; fault detection and diagnosis; fault-tolerant control; microgrids; smart grids
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increased interconnectivity of modern power grids through cyber-physical systems has made them vulnerable to cyberattacks, which can disrupt their operation and cause widespread outages. Given the potential for such attacks to cause significant damage to life and the economy, protecting power grids from cyberthreats is a high-priority issue. In this context, this Special Issue seeks to highlight the use of machine learning, which has proven effective in detecting and mitigating data anomalies, as a means of addressing cyberthreats against power grids. The issue will feature up-to-date research articles on machine learning implementation and achievements in cyberthreat detection and mitigation in power grids.
Topics of interest include, but are not limited to, the following areas:
- Cybersecurity dataset generation;
- Power grid vulnerability diagnosis;
- Cybersecurity testing;
- Privacy protection and federated networks;
- Anomaly detection;
- Intrusion detection systems;
- Wide-area monitoring and protection systems;
- SCADA systems;
- Control systems;
- Recognition and avoidance of phishing;
- Malware identification;
- Intrusion prevention systems;
- Denial of services attacks;
- Time synchronization attacks against phasor measurement units;
- Time delay attack;
- Attacks in wireless sensor grids;
- Stealthy cyberattack detection;
- Electricity theft detection;
- Black box attacks;
- Line failure attack detection;
- Automatic attack filtering.
Prof. Dr. Mohamed Benbouzid
Dr. Tarek Berghout
Prof. Dr. Yassine Amirat
Guest Editors
Manuscript Submission Information
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