Next-Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Modelsopen access
- Authors
- Kilichev, Dusmurod; Turimov, Dilmurod; Kim, Wooseong
- Issue Date
- Feb-2024
- Publisher
- MDPI
- Keywords
- convolutional neural network; cybersecurity; deep learning; Edge-IIoTset; electric vehicle charging station; ensemble learning; gated recurrent unit; Internet of Things; intrusion detection system; long short-term memory
- Citation
- MATHEMATICS, v.12, no.4
- Journal Title
- MATHEMATICS
- Volume
- 12
- Number
- 4
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90796
- DOI
- 10.3390/math12040571
- ISSN
- 2227-7390
2227-7390
- Abstract
- In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90796)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.