Abstract
The evolution from traditional power grids to smart grids, driven by the wide adoption and rapid integration of advanced sensing and communication technologies, introduces new business opportunities alongside critical technical challenges, particularly in the realm of cyber resilience in disaster situations. Coping with the increasing spectrum of cyber threats and their sophisticated evasion techniques is among the most critical challenges due to the devastating impact on individuals and the society in case of a successful attack. Therefore, we propose in this paper a robust intrusion detection system (IDS) specifically designed for smart grid environment and constraints. This IDS employs convolutional neural network (CNN) method to effectively identify and neutralise potential security threats, and the obtained evaluation results are promising. Moreover, our CNN model is complemented by incorporating the SHapley Additive exPlanations (SHAP) algorithm to improve the transparency of the decision-making process.
| Original language | English |
|---|---|
| Title of host publication | 2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) |
| Publisher | IEEE |
| Pages | 1-7 |
| Number of pages | 7 |
| ISBN (Electronic) | 979-8-3503-6792-8 |
| ISBN (Print) | 979-8-3503-6793-5 |
| DOIs | |
| Publication status | E-pub ahead of print - 18 Dec 2024 |
| Event | 2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM 2024): ICT-DM 2024 - Setif 1 University - Ferhat ABBAS, Setif, Algeria Duration: 19 Nov 2024 → 21 Nov 2024 https://ict-dm2024.univ-setif.dz/ |
Publication series
| Name | |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 2469-8822 |
| ISSN (Electronic) | 2643-6868 |
Conference
| Conference | 2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM 2024) |
|---|---|
| Country/Territory | Algeria |
| City | Setif |
| Period | 19/11/24 → 21/11/24 |
| Internet address |
Bibliographical note
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This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
Keywords
- Additives
- Disasters
- Neural networks
- Intrusion detection
- Smart grids
- Sensors
- Information and communication technology
- Convolutional neural networks
- Security
- Resilience
