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Academic Publication Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid
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Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid
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Multi-objective optimization of federated intrusion detection system for detecting black hole and gray hole attacks in MANET
Mobile Ad Hoc Networks (MANETs) are networks that do not have a permanent infrastructure, and are vulnerable to attacks like Black Hole Attacks (BHA) and Gray Hole Attacks (GHA) in which packets ar...
A Comprehensive Review of Real-Time Feeder Monitoring and Auditing Systems: Architectures, Technologies, and Analytics for the Smart Grid
Abstract: The modernization of power distribution networks into intelligent Smart Grids necessitates a paradigm shift from periodic, manual inspections to continuous, real-time feeder monitoring an...
AI-DRIVEN FAULT DETECTION AND PREDICTIVE MAINTENANCE IN ELECTRICAL POWER SYSTEMS: A SYSTEMATIC REVIEW OF DATA-DRIVEN APPROACHES, DIGITAL TWINS, AND SELF-HEALING GRIDS
The increasing complexity of electrical power systems necessitates advanced fault detection and predictive maintenance strategies to enhance operational efficiency and grid reliability. Traditional...
Enhancing intrusion detection: a hybrid machine and deep learning approach
AbstractThe volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile...
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Are there open-source GitHub repositories related to Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid?
Yes, open-source projects like kitft/natural_language_autoencoders () are actively building upon these concepts.
What other academic literature is closely related to 'Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid'?
Yes, highly correlated activity was mapped. An entry titled 'Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid' discusses this: No description provided.
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