Biblio

In the past couple of years, railway infrastructure has been growing more connected, resembling more of a traditional Cyber-Physical System model. Due to the tightly coupled nature between the cyber and physical domains, new attack vectors are emerging that create an avenue for remote hijacking of system components not designed to withstand such attacks. As such, best practice cybersecurity techniques need to be put in place to ensure the safety and resiliency of future railway designs, as well as infrastructure already in the field. However, traditional large-scale experimental evaluation that involves evaluating a large set of variables by running a design of experiments (DOE) may not always be practical and might not provide conclusive results. In addition, to achieve scalable experimentation, the modeling abstractions, simulation configurations, and experiment scenarios must be designed according to the analysis goals of the evaluations. Thus, it is useful to target a set of key operational metrics for evaluation and configure and extend the traditional DOE methods using these metrics. In this work, we present a metrics-driven evaluation approach for evaluating the security and resilience of railway critical infrastructure using a distributed simulation framework. A case study with experiment results is provided that demonstrates the capabilities of our testbed.

Cyber-Physical Systems (CPS) consist of embedded computers with sensing and actuation capability, and are integrated into and tightly coupled with a physical system. Because the physical and cyber components of the system are tightly coupled, cyber-security is important for ensuring the system functions properly and safely. However, the effects of a cyberattack on the whole system may be difficult to determine, analyze, and therefore detect and mitigate. This work presents a model based software development framework integrated with a hardware-in-the-loop (HIL) testbed for rapidly deploying CPS attack experiments. The framework provides the ability to emulate low level attacks and obtain platform specific performance measurements that are difficult to obtain in a traditional simulation environment. The framework improves the cybersecurity design process which can become more informed and customized to the production environment of a CPS. The developed framework is illustrated with a case study of a railway transportation system.

Cyber-Physical Systems (CPS) are systems with seamless integration of physical, computational and networking components. These systems can potentially have an impact on the physical components, hence it is critical to safeguard them against a wide range of attacks. In this paper, it is argued that an effective approach to achieve this goal is to systematically identify the potential threats at the design phase of building such systems, commonly achieved via threat modeling. In this context, a tool to perform systematic analysis of threat modeling for CPS is proposed. A real-world wireless railway temperature monitoring system is used as a case study to validate the proposed approach. The threats identified in the system are subsequently mitigated using National Institute of Standards and Technology (NIST) standards.

Cyber-Physical Systems (CPS) are systems with seamless integration of physical, computational and networking components. These systems can potentially have an impact on the physical components, hence it is critical to safeguard them against a wide range of attacks. In this paper, it is argued that an effective approach to achieve this goal is to systematically identify the potential threats at the design phase of building such systems, commonly achieved via threat modeling. In this context, a tool to perform systematic analysis of threat modeling for CPS is proposed. A real-world wireless railway temperature monitoring system is used as a case study to validate the proposed approach. The threats identified in the system are subsequently mitigated using National Institute of Standards and Technology (NIST) standards.