Modern Autonomous Vehicles (AVs) leverage road context information collected through sensors (e.g., LiDAR, radar, and camera) to support the automated driving experience. Once such information is collected, a neural network model predicts subsequent actions that the AV executes. However, state-of-the-art research findings have shown the possibility that an attacker can compromise the accuracy of the neural network model in predicting tasks. Indeed, mispredicting the subsequent actions can cause harmful consequences to the road user's safety. In this paper, we analyze the disruptive impact of adversarial attacks on road context-aware Intrusion Detection System (RAIDS) and propose a solution to mitigate such effects. To this end, we implement five state-of-the-art evasion attacks on vehicle camera images the IDS uses to monitor internal vehicular traffic. Our experimental results underline how this type of attack can reduce the attack detection accuracy of such detectors down to 2.83%. To combat such adversarial attacks, we investigate different countermeasure and propose PAID, a robust context-aware IDS that leverage feature squeezing and GPS to detect intrusions. We evaluate PAID's capability in identifying such attacks, and implementation results confirm that PAID achieves a detection accuracy of up to 93.9%, outperforming RAIDS's performance.

Modern vehicles comprise hundreds of Electronic Control Units (ECUs) and sensors for enhancing numerous security and comfort-related functionalities. The ECUs perform real-time information exchange, such as automotive instructions over the Controller Area Network (CAN) bus. However, the CAN bus architecture supports very limited security features. Thus, in-vehicle communications over CAN are vulnerable to critical security threats. Also, as ECUs are resource-constrained in nature, the continuous message transmissions lead to drain out of energy during inter-ECU communication if the authentication scheme is not cost-effective. This paper proposes AutoSec, a lightweight scheme, exploiting low-cost bit-wise XOR and concatenation operations to facilitate secure and efficient in-vehicle communications for connected vehicles. We show through qualitative analysis that AutoSec preserves the security properties of message integrity, user authentication, and message confidentiality. We implemented AutoSec on Raspberry Pi 3B+ and performed exhaustive experiments to validate the security robustness and lightweightness of AutoSec. The results show that AutoSec reduces the computation time by and energy consumption by .