Multistage Intrusion Detection Framework Using a Robust Nonlinear Machine Learning Approach for Enhancing Cloud Security in Electric Vehicles in Smart Grid
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 2 |
| Year of Publication : 2025 |
| Authors : S. Selvakumari, K. Prabhakar, S. Selvakumaran, Mythili Nagalingam, C. Tamilselvi, T.A. Mohanaprakash |
10.14445/23488549/IJECE-V12I2P114
How to Cite?
S. Selvakumari, K. Prabhakar, S. Selvakumaran, Mythili Nagalingam, C. Tamilselvi, T.A. Mohanaprakash, "Multistage Intrusion Detection Framework Using a Robust Nonlinear Machine Learning Approach for Enhancing Cloud Security in Electric Vehicles in Smart Grid," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 2, pp. 152-165, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I2P114
Abstract:
Ensuring resilient and reliable operations requires addressing new Cloud security risks brought out by the integration of Electric Vehicles (EVs) into the ever-changing smart grids. Regarding energy distribution, vehicle operations, and public safety, the importance of a secure infrastructure in this context is essential, a single attack might cause major disruptions. However, designing effective Intrusion Detection Systems (IDS) is made more difficult by the dynamic and distributed nature of smart grids and the increasing complexity of cyberattacks. Within smart grids and smart cities, there is a multi-stage system called the Multitiered Intrusion Detection Framework utilising the Machine Learning Approach (MIDF-MLA) that aims to detect and mitigate attacks targeting EVs. To overcome these challenges, this paper proposes the MIF-MLA, using a strong, nonlinear machine learning model that can adapt to new threats by improving detection accuracy and decreasing false positives. The multi-stage architecture of MIDF-MLA is designed to address a wide range of attack vectors, including not limited to Distributed Denial-of-Service (DDoS) attacks, spoofing, and data manipulation during execution, ensuring robust system-wide Cloud security The proposed architecture has several potential uses, such as real-time monitoring of electric vehicle communication networks, anomaly detection in grid operations, and the creation of proactive defensive systems for critical infrastructure, such as power distribution nodes and charging stations, within interconnected smart communities. Validation of the efficacy of MIDF-MLA is accomplished through the utilisation of extensive simulation analysis. This investigation shows that MIDF-MLA can boost Cloud security, optimise resource allocation, and keep the system intact under several assault scenarios. This framework lays the platform for future advancements in electric vehicle protection within the broader context of smart grids.
Keywords:
Intrusion Detection, Nonlinear, Machine Learning, Cloud security, Electric, Vehicles, Smart Grids, DDoS.
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