ArticleSensors (Basel, Switzerland)2022
HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm.Scientific reports · 2025Article
- Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2025Review
- A secure and efficient deep learning-based intrusion detection framework for the internet of vehicles.Scientific reports · 2025Article
- Metaheuristic optimized complex-valued dilated recurrent neural network for attack detection in internet of vehicular communications.PeerJ. Computer science · 2024Article
- Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles.Sensors (Basel, Switzerland) · 2023Article
- RSU-Based Online Intrusion Detection and Mitigation for VANET.Sensors (Basel, Switzerland) · 2022Article
- A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets-a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.
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