.png){kind=icon}
返回
Metaheuristics based dimensionality reduction with deep learning driven false data injection attack detection for enhanced network security
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-69806-5.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements. These approaches make certain assumptions regarding false data injection (FDI) attacks, which are inadequate and restrictive to cope with cyberattacks. The reliance on communication technology has emphasized the exposure of power systems to FDI assaults that can bypass the current bad data detection (BDD) mechanism. The current study on unobservable FDI attacks (FDIA) reveals the severe threat of secured system operation because these attacks can avoid the BDD method. Thus, a Data-driven learning-based approach helps detect unobservable FDIAs in distribution systems to mitigate these risks. This study presents a new Hybrid Metaheuristics-based Dimensionality Reduction with Deep Learning for FDIA (HMDR-DLFDIA) Detection technique for Enhanced Network Security. The primary objective of the HMDR-DLFDIA technique is to recognize and classify FDIA attacks in the distribution systems. In the HMDR-DLFDIA technique, the min-max scalar is primarily used for the data normalization process. Besides, a hybrid Harris Hawks optimizer with a sine cosine algorithm (hybrid HHO-SCA) is applied for feature selection. For FDIA detection, the HMDR-DLFDIA technique utilizes the stacked autoencoder (SAE) method. To improve the detection outcomes of the SAE model, the gazelle optimization algorithm (GOA) is exploited. A complete set of experiments was organized to highlight the supremacy of the HMDR-DLFDIA method. The comprehensive result analysis stated that the HMDR-DLFDIA technique performed better than existing DL models.
Keyword:
False data injection
Deep learning
Metaheuristics
Harris hawks optimizer
Network security
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:
3.9
论文数:
28.0W
被引数:
83.5W
机构
引用论文
False data injection attacks detection with modified temporal multi-graph convolutional network in smart grids基于改进时态多图卷积网络的智能电网虚假数据注入攻击检测.png)
COMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏Distributed synchronous detection for false data injection attack in cyber-physical microgrids面向网络物理微网虚假数据注入攻击的分布式同步检测
PREAI
分享收藏A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoTINTERNET OF THINGS
INTERNET OF THINGSIF7.6
PREAI
分享收藏On Feasibility of Coordinated Time-Delay and False Data Injection Attacks on Cyber-Physical Systems
PREAI
分享收藏
查看更多内容