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Journal of Cybersecurity and Information Management
Volume 8 , Issue 2, PP: 60-68 , 2021 | Cite this article as | XML | Html |PDF

Title

Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach

  Ahmed N. Al-Masri 1 * ,   Hamam Mokayed 2

1  American University in the Emirates, Dubai, UAE
    (ahmed.almasri@aue.ae)

2  LTU University of Technology, Sweden
    (Hamam.mokayed@ltu.se)


Doi   :   https://doi.org/10.54216/JCIM.080203

Received: May 15, 2021 Accepted: October 16, 2021

Abstract :

With the increasing number of connected vehicles on the road, the need for secure in-vehicle systems is more pressing than ever. In-vehicle attacks can compromise the safety and privacy of drivers and passengers, and the detection of such attacks is crucial to prevent potential harm. In this paper, we propose an applied deep learning approach for detecting in-vehicle attacks. Our approach is based on a gated recurrent unit (GRU) that is trained on a dataset of network traffic collected from in-vehicle communication systems. We evaluate our approach on a real-world dataset and demonstrate its effectiveness in detecting different types of in-vehicle attacks, including denial of service (DoS), remote replay attacks, and flooding attacks. Our results show that the proposed approach can achieve high accuracy in detecting in-vehicle attacks. We also compare our approach with traditional machine learning algorithms and show that our approach outperforms them in terms of accuracy. Our proposed approach can be used as a standalone system or as a complementary solution to existing in-vehicle security systems to enhance the overall cybersecurity of connected vehicles.

Keywords :

In-Vehicle Attacks; Deep Learning; Information Security; Internet of Vehicles

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Cite this Article as :
Style #
MLA Ahmed N. Al-Masri, Hamam Mokayed. "Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach." Journal of Cybersecurity and Information Management, Vol. 8, No. 2, 2021 ,PP. 60-68 (Doi   :  https://doi.org/10.54216/JCIM.080203)
APA Ahmed N. Al-Masri, Hamam Mokayed. (2021). Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach. Journal of Journal of Cybersecurity and Information Management, 8 ( 2 ), 60-68 (Doi   :  https://doi.org/10.54216/JCIM.080203)
Chicago Ahmed N. Al-Masri, Hamam Mokayed. "Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach." Journal of Journal of Cybersecurity and Information Management, 8 no. 2 (2021): 60-68 (Doi   :  https://doi.org/10.54216/JCIM.080203)
Harvard Ahmed N. Al-Masri, Hamam Mokayed. (2021). Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach. Journal of Journal of Cybersecurity and Information Management, 8 ( 2 ), 60-68 (Doi   :  https://doi.org/10.54216/JCIM.080203)
Vancouver Ahmed N. Al-Masri, Hamam Mokayed. Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach. Journal of Journal of Cybersecurity and Information Management, (2021); 8 ( 2 ): 60-68 (Doi   :  https://doi.org/10.54216/JCIM.080203)
IEEE Ahmed N. Al-Masri, Hamam Mokayed, Detecting In-Vehicle Attacks with Deep Learning: An Applied Approach, Journal of Journal of Cybersecurity and Information Management, Vol. 8 , No. 2 , (2021) : 60-68 (Doi   :  https://doi.org/10.54216/JCIM.080203)