Enhancing cybersecurity in virtual power plants by detecting network based cyber attacks using an unsupervised autoencoder approach

The increasing adoption of the Internet of Things (IoT) in energy systems has brought significant advancements but also heightened cyber security risks. Virtual Power Plants (VPPs), which aggregate distributed renewable energy resources into a single entity for participation in energy markets, are p...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 32374 - 23
Hlavní autoři:	Singh, Kumari Nutan, Goswami, Arup Kumar, Chudhury, Nalin Behari Dev, Shuaibu, Hassan Abdurrahman, Ustun, Taha Selim
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 05.09.2025 Nature Publishing Group Nature Portfolio
Témata:	639/166/4073 639/166/987 639/4077/4073 639/4077/909 Algorithms Alternative energy sources Architecture Auto-encoder (AE) Communication Consumers Cyber security Cybersecurity Deep learning Denial of service attacks Energy consumption Energy market (EM) Energy resources Energy storage False data injection attack (FDIA) Humanities and Social Sciences Infrastructure Internet of Things Interoperability Machine learning multidisciplinary Neural networks Power plants Privacy Renewable energy sources Renewable energy sources (RES) Renewable resources Science Science (multidisciplinary) Smart grid technology Software Virtual power plant (VPP) Virtual power plant (VPP) Renewable energy sources (RES) Energy market (EM) Cyber security False data injection attack (FDIA) Auto-encoder (AE)
ISSN:	2045-2322, 2045-2322
On-line přístup:	Získat plný text
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