.png){kind=icon}
返回
Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm
.png){kind=icon}
OA
AI
DOI：10.3390/s22103933.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Distributed Energy Resources (DERs) are growing in importance Power Systems. Battery Electrical Storage Systems (BESS) represent fundamental tools in order to balance the unpredictable power production of some Renewable Energy Sources (RES). Nevertheless, BESS are usually remotely controlled by SCADA systems, so they are prone to cyberattacks. This paper analyzes the vulnerabilities of BESS and proposes an anomaly detection algorithm that, by observing the physical behavior of the system, aims to promptly detect dangerous working conditions by exploiting the capabilities of a particular neural network architecture called the autoencoder. The results show the performance of the proposed approach with respect to the traditional One Class Support Vector Machine algorithm.
Keyword:
cybersecurity
distributed energy resources
electrical battery storage systems
neural network
autoencoder
anomaly detection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
.png){kind=icon}
分享收藏Secure smart contract-enabled control of battery energy storage systems against cyber-attacks针对网络攻击的电池储能系统的安全智能合约控制OAAI
OAAI分享收藏
分享收藏
查看更多内容