DDoS attack detection in smart grid network using reconstructive machine learning models

Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous c...

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Vydáno v:	PeerJ. Computer science Ročník 10; s. e1784
Hlavní autoři:	Naqvi, Sardar Shan Ali, Li, Yuancheng, Uzair, Muhammad
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States PeerJ. Ltd 09.01.2024 PeerJ, Inc PeerJ Inc
Témata:	Accuracy Algorithms Analysis Artificial Intelligence Auto-encoder Classification Computer Networks and Communications Critical infrastructure Cyber security Data Mining and Machine Learning Datasets DDoS attack detection Decision trees Deep learning Denial of service attacks Disruption Game theory Infrastructure (Economics) Machine learning Neural Networks Reconstructive machine learning Security and Privacy Smart grid Smart Grid protection State estimation Support vector machines Reconstructive machine learning Auto-encoder DDoS attack detection Deep auto-encoder Intrusion detection Threat mitigation Cyber security Smart grid Extreme learning machine (ELM) autoencoder Smart Grid protection
ISSN:	2376-5992, 2376-5992
On-line přístup:	Získat plný text
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