CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks

Abstract

The main goal of the H2020-CARAMEL project is to address the cybersecurity gaps introduced by the new technological domains adopted by modern vehicles applying, among others, advanced Artificial Intelligence and Machine Learning techniques. As a result, CARAMEL enhances the protection against threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture aiming at assessing the integrity of the information transmitted by vehicles, as well as at improving the security and privacy of communication for connected and autonomous driving. The proposed architecture includes: (1) multi-radio access technology capabilities, with simultaneous 802.11p and LTE-Uu support, enabled by the connectivity infrastructure; (2) a MEC platform, where, among others, algorithms for detecting attacks are implemented; (3) an intelligent On-Board Unit with anti-hacking features inside the vehicle; (4) a Public Key Infrastructure that validates in real-time the integrity of vehicle’s data transmissions. As an indicative application, the interaction between the entities of the CARAMEL architecture is showcased in case of a GPS spoofing attack scenario. Adopted attack detection techniques exploit robust in-vehicle and cooperative approaches that do not rely on encrypted GPS signals, but only on measurements available in the CARAMEL architecture.

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

“…Such malicious GPS signals can be verified via the location information retrieved from the base station [154]. However, security vulnerabilities that exist in IEEE 802.11p and C-V2X technologies can lead to geolocation data poisoning attacks in cases of verifying valid GPS satellite information via RSUs or base stations [116], [154]. The severity of GPS spoofing attack is generally moderate, but it can be particularly critical for high-level AVs.…”

Countering Adversarial Attacks on Autonomous Vehicles Using Denoising Techniques: A Review

“…Saturating/Jamming the lidar [18], blinding the camera [18], spoofing the signal of GPS [18] or the lidar [17] are some of the attacks that can have a significant effect on the vehicle's behavior. For instance, Vitale et al [24] focus on the H2020-CARAMEL project, which addresses the cybersecurity gaps introduced by the new technological domains. More specifically, in [24], the potential threats and vulnerabilities on the vehicle's sensors, i.e., the GPS receiver, have been presented.…”

“…For instance, Vitale et al [24] focus on the H2020-CARAMEL project, which addresses the cybersecurity gaps introduced by the new technological domains. More specifically, in [24], the potential threats and vulnerabilities on the vehicle's sensors, i.e., the GPS receiver, have been presented. In sensor attacks, it is assumed that the attacker is outside the vehicle and targets sensor data acquisition.…”

Alternating optimization for multimodal collaborating odometry estimation in CAVs

“…Once again, each vehicle estimates only its own state. Localizing either vehicles or unmanned aerial vehicles in the presence of position outliers caused by cyberattacks or GPSdenied environments are discussed in [7] and [8]. The former formulates a centralized convex optimization problem in order to robustify position estimations, while the latter makes use of the renowned multi-dimensional scaling algorithm for centralized inter-and intra-cluster relative localization.…”