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.
MDPI and ACS Style
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
AMA Style
Kilichev D, Turimov D, Kim W. Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models. Mathematics. 2024; 12(4):571. https://doi.org/10.3390/math12040571
Chicago/Turabian Style
Kilichev, Dusmurod, Dilmurod Turimov, and Wooseong Kim. 2024. "Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models" Mathematics 12, no. 4: 571. https://doi.org/10.3390/math12040571