Title: Intrusion detection method for GPS based on deep learning for autonomous vehicle

Authors: Boughanja Manale; Tomader Mazri

Addresses: Advanced Systems Engineering, Electrical Engineering, Networks and Telecommunications System, Ibn Tofail Science University, Kenitra, Morocco ' Advanced Systems Engineering, Electrical Engineering, Networks and Telecommunications System, Ibn Tofail Science University, Kenitra, Morocco

Abstract: Protecting an environment in perpetual motion will be difficult to be secured against attacks, and also challenging to detect threats. The intrusion will result in serious security risks. With the refinement of the attacker's skills, new intrusions pose serious problems. To enhance security measurements must be implemented. The intrusion detection system (IDS) is a relevant innovation, which checks the system's activity to detect any suspicious behaviour that may indicate that the system has been attacked or misused. We outlined the key design of autonomous AV keys and their challenges. Most technology has been used as machine learning techniques but it was only used for the processing of applications based on imagery. In this study, we have proposed a model to secure the GPS sensor. The model implements the deep learning technique to predict vehicle behaviour as a function of location. Our model helps to improve the accuracy and scalability of the vehicle.

Keywords: security; detection; deep learning; algorithms; intrusion detection system.

DOI: 10.1504/IJESDF.2022.120039

International Journal of Electronic Security and Digital Forensics, 2022 Vol.14 No.1, pp.37 - 52

Received: 12 Dec 2020
Accepted: 12 Apr 2021
Published online: 04 Jan 2022 *

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