Securing Autonomous Vehicles Against GPS Spoofing Attacks: A Deep Learning Approach
Maliha Shabbir, Mohsin Kamal, Zahid Ullah, Maqsood Muhammad Khan
Abstract
With the rapid advancement of technology and multimedia systems, ensuring security has become a critical concern. Connected and Autonomous Vehicles (CAVs) are vulnerable to various hacking techniques, including jamming and spoofing. Global Positioning System (GPS) location spoofing poses a significant threat to CAVs, compromising their security and potentially endangering pedestrians and drivers. To address this issue, this research proposes a novel methodology that uses deep learning (DL) algorithms, such as Convolutional Neural Networks (CNN), and machine learning (ML) algorithms, such as Support Vector Machine (SVM), to protect CAVs from GPS location spoofing attacks. The proposed solution is validated using real-time simulations in the CARLA simulator, and extensive analysis of different learning algorithms is conducted to identify the most suitable approach across three distinct trajectories. Training and testing data include GPS coordinates, spoofed coordinates, and localization algorithm values. The proposed machine learning algorithm achieved 99% and 96% accuracy for the best and worst case scenarios, respectively. In case of deep learning, it achieved as high as 99% for best and 82% for the worst case scenario.