Detection and mitigation techniques against eavesdropping and spoofing in GPS

As a significant CPS application, V2X networks have their own specific security atmosphere in addition to their latency and energy efficiency requirements. There are two critical attack types against secure V2X deployments, which are eavesdropping and spoofing attacks. In this thesis, we evaluate these attacks in a novel system model, where aerial spoofers, ground-located spoofers, and eavesdroppers are jointly integrated. Against eavesdropper attacks, an error vector is added to the information data to falsify the attackers using FEC codes with respect to and relay-aided McEliece cryptosystem-based methodology. The results show that significantly noisier channels between relays and the eavesdropper enhance information security. Additionally, the impacts of time spoofing attacks on satellite positioning signals are studied. For this purpose, two pseudorange-based spoofing detection algorithms with different performances and complexity levels are proposed to mitigate ground-located spoofers. In order to find the best detection thresholds of these algorithms, Pareto fronts are plotted, and then a decision tree-based detection approach is analyzed. These algorithms are also tested against novel aerial spoofers, which suffer additional mobility and atmospheric errors. Finally, a high-level hybrid decision methodology is applied to improve spoofing detection rates and reduce false alarm rates by integrating a decision fusion module. With this approach, the decision fusion module combines individual outcomes of the proposed algorithm and improves spoofing decision performance. In summary, this thesis proposes extensive strategies against eavesdropper and spoofing attacks in V2X networks by providing secure transmission schemes and detection algorithms in a novel system model.