In Vehicle-to-Vehicle (V2V) communications, malicious actors may spread false information to undermine the safety and efficiency of the vehicular traffic stream. Thus, vehicles must determine how to respond to the contents of messages which maybe false even though they are authenticated in the sense that receivers can verify contents were not tampered with and originated from a verifiable transmitter. Existing solutions to find appropriate actions are inadequate since they separately address trust and decision, require the honest majority (more honest ones than malicious), and do not incorporate driver preferences in the decision-making process. In this work, we propose a novel trust-aware decision-making framework without requiring an honest majority. It securely determines the likelihood of reported road events despite the presence of false data, and consequently provides the optimal decision for the vehicles. The basic idea of our framework is to leverage the implied effect of the road event to verify the consistency between each vehicle's reported data and actual behavior, and determine the data trustworthiness and event belief by integrating the Bayes' rule and Dempster Shafer Theory. The resulting belief serves as inputs to a utility maximization framework focusing on both safety and efficiency. This framework considers the two basic necessities of the Intelligent Transportation System and also incorporates drivers' preferences to decide the optimal action. Simulation results show the robustness of our framework under the multiple-vehicle attack, and different balances between safety and efficiency can be achieved via selecting appropriate human preference factors based on the driver's risk-taking willingness.
Drones raise significant privacy and security threats, by intruding into the airspace of private properties or unauthorized regions. Being able to detect and localize the encroaching drones is essential to build geofencing systems to prevent drone misuse. While most existing approaches focus on detecting and localizing active drones, passive drones that do not emit signals are particularly challenging to localize, without requiring advanced hardware. In this work, we propose a novel, low-cost passive drone localization approach, by leveraging opportunistic environmental RF signals (e.g., LTE or WiFi) that reflect off the target drone, with only a single wireless receiver. We implement a prototype system on a USRP-device based testbed, with standard LTE signals emitted by multiple distributed transmitters, and conduct experiments on top of a campus building to evaluate its performance. We also perform a drone detection range analysis to extrapolate the real-world applicability of our scheme.
CCS CONCEPTS• General and reference → Measurement; • Hardware → Digital signal processing; • Security and privacy → Intrusion detection systems; Mobile and wireless security.
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