Cyberattacks against industrial control systems have increased over the last decade, making it more critical than ever for system owners to have the tools necessary to understand the cyber resilience of their systems. However, existing tools are often qualitative, subject matter expertise-driven, or highly generic, making thorough, data-driven cyber resilience analysis challenging. The ADROC project proposed to develop a platform to enable efficient, repeatable, data-driven cyber resilience analysis for cyber-physical systems. The approach consists of two phases of modeling: computationally efficient math modeling and high-fidelity emulations. The first phase allows for scenarios of low concern to be quickly filtered out, conserving resources available for analysis. The second phase supports more detailed scenario analysis, which is more predictive of real-world systems. Data extracted from experiments is used to calculate cyber resilience metrics. ADROC then ranks scenarios based on these metrics, enabling prioritization of system resources to improve cyber resilience.
This report summarizes the activities performed as part of the Science and Engineering of Cybersecurity by Uncertainty quantification and Rigorous Experimentation (SECURE) Grand Challenge LDRD project. We provide an overview of the research done in this project, including work on cyber emulation, uncertainty quantification, and optimization. We present examples of integrated analyses performed on two case studies: a network scanning/detection study and a malware command and control study. We highlight the importance of experimental workflows and list references of papers and presentations developed under this project. We outline lessons learned and suggestions for future work.
Aging plants, efficiency goals, and safety needs are driving increased digitalization in nuclear power plants (NPP). Security has always been a key design consideration for NPP architectures, but increased digitalization and the emergence of malware such as Stuxnet, CRASHOVERRIDE, and TRITON that specifically target industrial control systems have heightened concerns about the susceptibility of NPPs to cyber attacks. The cyber security community has come to realize the impossibility of guaranteeing the security of these plants with 100% certainty, so demand for including resilience in NPP architectures is increasing. Whereas cyber security design features often focus on preventing access by cyber threats and ensuring confidentiality, integrity, and availability (CIA) of control systems, cyber resilience design features complement security features by limiting damage, enabling continued operations, and facilitating a rapid recovery from the attack in the event control systems are compromised. This paper introduces the REsilience VeRification UNit (RevRun) toolset, a software platform that was prototyped to support cyber resilience analysis of NPP architectures. Researchers at Sandia National Laboratories have recently developed models of NPP control and SCADA systems using the SCEPTRE platform. SCEPTRE integrates simulation, virtual hardware, software, and actual hardware to model the operation of cyber-physical systems. RevRun can be used to extract data from SCEPTRE experiments and to process that data to produce quantitative resilience metrics of the NPP architecture modeled in SCEPTRE. This paper details how RevRun calculates these metrics in a customizable, repeatable, and automated fashion that limits the burden placed upon the analyst. This paper describes RevRun's application and use in the context of a hypothetical attack on an NPP control system. The use case specifies the control system and a series of attacks and explores the resilience of the system to the attacks. The use case further shows how to configure RevRun to run experiments, how resilience metrics are calculated, and how the resilience metrics and RevRun tool can be used to conduct the related resilience analysis.