Citation: Ullah, S.; Khan, M.A.;
Ahmad, J.; Jamal, S.S.; e Huma, Z.;
Hassan, M.T.; Pitropakis, N.; Arshad;
Buchanan, W.J. HDL-IDS: A Hybrid
Deep Learning Architecture for
Intrusion Detection in the Internet of
Vehicles. Sensors 2022, 22, 1340.
https://doi.org/10.3390/s22041340
Academic Editor: Yuh-Shyan Chen
Received: 10 January 2022
Accepted: 8 February 2022
Published: 10 February 2022
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Article
HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion
Detection in the Internet of Vehicles
Safi Ullah
1
, Muazzam A. Khan
1,2
, Jawad Ahmad
3,
* , Sajjad Shaukat Jamal
4
, Zil e Huma
5
,
Muhammad Tahir Hassan
6
, Nikolaos Pitropakis
3
, Arshad
7
and William J. Buchanan
3
1
Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;
safiullah@cs.qau.edu.pk (S.U.); muazzam.khattak@qau.edu.pk (M.A.K.)
2
Pakistan Academy of Sciences, Islamabad 44000, Pakistan
3
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK;
n.pitropakis@napier.ac.uk (N.P.); b.buchanan@napier.ac.uk (W.J.B.)
4
Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia;
shussain@kku.edu.sa
5
Department of Electrical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan;
zilehuma@ist.edu.pk
6
Department of Mechanical Engineering, Bahauddin Zakariya University, Multan 66000, Pakistan;
tahirqureshi@bzu.edu.pk
7
Institute for Energy and Environment, University of Strathclyde, Glasgow G1 1XQ, UK;
arshad.100@strath.ac.uk
* Correspondence: J.Ahmad@napier.ac.uk
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.
Keywords:
deep learning; gated recurrent units; Internet of Things; Internet of Vehicles; long short-
term memory; machine learning
1. Introduction
Internet of Things (IoT) is an advanced technology that connects smart devices to
the internet, such as the Internet of Vehicles (IoV), wireless cameras, and other electronic
devices. Due to the rapid increase of connected vehicles, several security and privacy
challenges have been introduced [
1
–
3
]. A basic framework for communications between
vehicular networks is IoV [
4
]. It establishes a dependable network transmission between
vehicles [
5
]. The IoV network consists of two sub-networks—intra-vehicle network and
inter-vehicular network. The intra-vehicle network involves internal electronic devices
and sensors of a vehicle, which are connected to a centralized controller for message
transmission and performing a specific task [
6
]. While an inter-vehicular network connects
a vehicle to external devices using vehicle-to-everything (V2X) technology. V2X allows
Sensors 2022, 22, 1340. https://doi.org/10.3390/s22041340 https://www.mdpi.com/journal/sensors
