A credibility score algorithm for malicious data detection in urban vehicular networks

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control

V2C: A Trust-Based Vehicle to Cloud Anomaly Detection Framework for Automotive Systems

Vehicles have become connected in many ways. They communicate with the cloud and will use Vehicle-to-Everything (V2X) communication to exchange warning messages and perform cooperative actions such as platooning. Vehicles have already been attacked and will become even more attractive targets due to their increasing connectivity, the amount of data they produce and their importance to our society. It is therefore crucial to provide cyber security measures to prevent and limit the impact of attacks.As it is problematic for a vehicle to reliably assess its own state when it is compromised, we investigate how vehicle trust can be used to identify compromised vehicles and how fleet-wide attacks can be detected at an early stage using cloud data. In our proposed V2C Anomaly Detection framework, peer vehicles assess each other based on their perceived behavior in traffic and V2X-enabled interactions, and upload these assessments to the cloud for analysis. This framework consists of four modules. For each module we define functional demands, interfaces and evaluate solutions proposed in literature allowing manufacturers and fleet owners to choose appropriate techniques. We detail attack scenarios where this type of framework is particularly useful in detecting and identifying potential attacks and failing software and hardware. Furthermore, we describe what basic vehicle data the cloud analysis can be based upon

Research on computer aided testing of pilot response to critical in-flight events

Experiments on pilot decision making are described. The development of models of pilot decision making in critical in flight events (CIFE) are emphasized. The following tests are reported on the development of: (1) a frame system representation describing how pilots use their knowledge in a fault diagnosis task; (2) assessment of script norms, distance measures, and Markov models developed from computer aided testing (CAT) data; and (3) performance ranking of subject data. It is demonstrated that interactive computer aided testing either by touch CRT's or personal computers is a useful research and training device for measuring pilot information management in diagnosing system failures in simulated flight situations. Performance is dictated by knowledge of aircraft sybsystems, initial pilot structuring of the failure symptoms and efficient testing of plausible causal hypotheses

Tourist Impact in Acadia National Park: Investigating and analyzing tourist usage patterns on Park Loop Road to determine fee compliance solutions

This project investigated the problems associated with increased visitation within Acadia National Park. Specifically, the problem of unsatisfactory fee compliance associated with unmonitored entry to the park was addressed. To this end, pressure based axle counters were designed, built, and deployed at all points of access to Park Loop Road. Additionally, fee compliance was quantified in parking lots along Park Loop Road. Ultimately, a relationship was found between unmonitored entry and fee noncompliance, and a comprehensive solution was researched and proposed