ANOMALY-BASED INTRUSION DETECTION IN VEHICULAR NETWORKS USING GATED RECURRENT UNIT DEEP LEARNING MODEL-- A SYSTEMATIC REVIEW
DOI:
https://doi.org/10.33003/fjs-2025-0912-3993Keywords:
Gated Recurrent Unit (GRU), Vehicular Ad Hoc Networks (VANETs), Intrusion Detection System (IDS), Anomaly Detection, Deep Learning, Intelligent Transportation Systems (ITS), Recurrent Neural Networks (RNN), Cybersecurity, Controller Area Network (CAN), Real-time DetectionAbstract
Intelligent transportation systems (ITS) have been revitalized by the rapid growth of vehicular networks, namely Vehicular Ad Hoc Networks (VANETs), which have accelerated real-time vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) interactions. However, the progressive and interconnected qualities of these networks create major cybersecurity complications that typical traditional solutions cannot properly handle. Through a survey of peer-reviewed research, this systematic literature review investigates the application of Gated Recurrent Unit (GRU) deep learning architectures for anomaly-based intrusion detection within automotive networks, examining peer-reviewed studies that were released between 2021 and 2025. With an emphasis on GRU implementation, the paper divides deep learning (DL) approaches into five primary categorization divisions: Generative Models, Feed-Forward Neural Networks (FFNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Reinforcement Learning (DRL). Through intricate analysis, this study shows that GRU models are ideally suited for intrusion detection in vehicular networks, capturing temporal connections in contrast to conventional Long Short-Term Memory (LSTM) networks. Findings show that GRU techniques are notably effective at detecting a variety of attack types, such as DoS, fuzzing, spoofing, and impersonation attacks, while preserving low false positive rates (FPR) and computational complexity suitable for real-time deployment in vehicular environments with limited resources. GRU models have continuously outperformed conventional methods in evaluation measures like as accuracy, precision, recall, F1-score, and False Alarm Rate (FAR). According to the review's findings, GRU-based anomaly detection is a viable strategy for achieving vehicle network security with notably optimized efficiency.
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