Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models
Dusmurod Kilichev, Dilmurod Turimov, Wooseong Kim
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.