Citation:
Kilichev, D.; Turimov, D.;
Kim, W. Next–Generation Intrusion
Detection for IoT EVCS: Integrating
CNN, LSTM, and GRU Models.
Mathematics 2024, 12, 571. https://
doi.org/10.3390/math12040571
Academic Editor: Matjaz Perc
Received: 6 January 2024
Revised: 9 February 2024
Accepted: 12 February 2024
Published: 14 February 2024
Copyright:
© 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
mathematics
Article
Next–Generation Intrusion Detection for IoT EVCS:
Integrating CNN, LSTM, and GRU Models
Dusmurod Kilichev
, Dilmurod Turimov
and Wooseong Kim *
Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea;
dusmurod@gachon.ac.kr (D.K.); dilmurod@gachon.ac.kr (D.T.)
*
Correspondence: wooseong@gachon.ac.kr
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
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