aspg-2-1750-1791417284 20261007235444 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2021 8 2 Ahmed N. Al-Masri American University in the Emirates, Dubai, UAE Hamam Mokayed LTU University of Technology, Sweden 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. 2021 60 68 1750 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.080203 https://www.americaspg.com/journal/2/article/1750 https://www.americaspg.com/storage/91683176657.pdf