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HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

Ullah, Safi; Khan, Muazzam A.; Ahmad, Jawad; Jamal, Sajjad Shaukat; e Huma, Zil; Hassan, Muhammad Tahir; Pitropakis, Nikolaos; Arshad; Buchanan, William J.


Safi Ullah

Muazzam A. Khan

Sajjad Shaukat Jamal

Zil e Huma

Muhammad Tahir Hassan



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.

Journal Article Type Article
Acceptance Date Feb 8, 2022
Online Publication Date Feb 10, 2022
Publication Date 2022
Deposit Date Feb 12, 2022
Publicly Available Date Feb 14, 2022
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 22
Issue 4
Article Number 1340
Keywords deep learning; gated recurrent units; Internet of Things; Internet of Vehicles; long short-term memory; machine learning
Public URL


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