Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Dusmurod Kilichev, Dilmurod Turimov and Wooseong Kim ()
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Dusmurod Kilichev: Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
Dilmurod Turimov: Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
Wooseong Kim: Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea

Mathematics, 2024, vol. 12, issue 4, 1-26

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.

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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