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A dual-head output network attack detection and classification approach for multi-energy systems

Authors :
Tong Li
Xiaoyu Zhang
Hai Zhao
Jiachen Xu
Yiming Chang
Shujun Yang
Source :
Frontiers in Energy Research, Vol 12 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

In today’s digital age, multi-energy systems (MES) have become an indispensable part of the social infrastructure, providing people with diversified energy support such as electricity, gas, water and so on. However, with the increasing popularity and networking of MES, the network security threats they face are becoming more and more serious, especially the threat of network attacks. This makes it essential to detect attacks on MES and precisely classify attack types in order to establish effective defense strategies. In this paper, a Dual-Head output network attack detection and classification method based on parallel CNN-BiLSTM network is proposed. The method adopts a parallel structure and can process different aspects of information at the same time, speeding up the training and inference process of the whole network, making the system respond more quickly to potential network attacks, and improving real-time and efficiency. The multi-model fusion structure can give full play to the advantages of CNN and BiLSTM in processing different types of data, so that the system can capture attack characteristics more comprehensively in many aspects, and improve the overall detection and classification performance. The dual-head output not only improves the system’s ability to accurately detect attacks, but also can effectively classify different types of attacks in detail, which helps to formulate more targeted defense strategies. In addition, in order to effectively evaluate our proposed method, the network traffic data required for the experiment were collected in an environment very similar to the actual operating environment of a multi-energy system. Finally, the experiment verifies that our method can not only realize effective detection of network attacks, but also accurately classify different types of attacks.

Details

Language :
English
ISSN :
2296598X
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Energy Research
Publication Type :
Academic Journal
Accession number :
edsdoj.fedf9098e6f24976b51db755b06f7c10
Document Type :
article
Full Text :
https://doi.org/10.3389/fenrg.2024.1367199