Embedded computing devices play an integral role in the mechanical operations of modern-day vehicles. These devices exchange information containing critical vehicle parameters that reflect the current state of operations. Such information can be captured for various purposes, such as diagnostics, fleet management, and analytics. Although monitoring individual parameters can be useful for some applications, monitoring distinct combinations of parameters can reveal more complex and higher-level states that may give useful information. Existing monitoring systems either lack user configurability and control or present simple user interfaces that make it difficult to monitor and collate different parameters to observe high-level vehicle states. In this work, we present TruckSTM, a novel application that realizes user-defined states from messages seen in the embedded networks of medium and heavy duty vehicles and displays state transitions on an interactive user interface. We begin by symbolically formulating some of the in-vehicle networking concepts and formally defining the concept of operational states and state transitions. We then elaborate on the operations performed by TruckSTM in mapping network-obtained vehicle parameters to states that can be defined in standard JSON format. Finally, we evaluate TruckSTM’s asymptotic performance and present the results for the worst-case scenario and demonstrate that in a real world scenario such high level state visualization constraints of an operational truck.

Vehicle security has been receiving a lot of attention from both the black hat and white hat community of late. Research in this area has already led to the fabrication of different attacks, of which some have been shown to have potentially grave consequences. Vehicle vendors and original equipment manufacturers (OEM)s are thus presented with the additional responsibility of ensuring in-vehicular communication level security. In this poster paper, we present a framework, which allows any individual to write, test, and store exploit scripts which could then be run by any interested party on in-vehicular networks of commercial vehicles like trucks and buses.

Vehicles now include Electronic Control Units (ECUs) that communicate with each other via broadcast networks. Cyber-security professionals have shown that such embedded communication networks can be compromised. Very recently, it has been shown that embedded devices connected to commercial vehicle networks can be manipulated to perform unintended actions by injecting spoofed messages. Such attacks can be hard to detect as they can mimic safety critical actions performed by ECUs. We present a precedence graph-based anomaly detection technique to detect malicious message injections. Our approach can detect malicious message injections and is able to distinguish them from safety critical actions like hard braking.

User studies to investigate which human traits affect a user's response to cyber security related situations are typically conducted via self-reported surveys. However, it has been observed that factors such as peer perception, socially desirable responding, and responder bias etc. frequently impact the results, which then do not necessarily reflect the actual behavior of the user when subjected to real world security incidents. To mitigate such biases, we developed PsychoRithm - a software system that presents different real-world security scenarios for the subjects and records their real-time reactions to these scenarios. This paper describes the architecture of PsychoRithm, the design choices we had to make, and the challenges we faced in the design process.