Spotlight on Lablet Research #13 - Multi-Model Testbed for the Simulation-Based Evaluation of Resilience
Spotlight on Lablet Research #13 -
Project: Multi-Model Testbed for the Simulation-Based Evaluation of Resilience
Lablet: Vanderbilt University
The goal of the Multi-model Testbed is to provide a collaborative design tool for evaluating various cyberattack/defense strategies and their effects on the physical infrastructure. The web-based, cloud-hosted environment integrates state-of-the-art simulation engines for the different CPS domains and presents interesting research challenges as ready-to-use scenarios. Input data, model parameters, and simulation results are archived, versioned with a strong emphasis on repeatability and provenance.
Earlier researchers developed the Science of SecUre and REsilient Cyber-Physical Systems (SURE) platform, a modeling and simulation integration testbed for evaluation of resilience for complex CPS. Previous efforts resulted in a web-based collaborative design environment for attack-defense scenarios supported by a cloud-deployed simulation engine for executing and evaluating the scenarios. Led by PI Peter Volgyesi and Co-PI Himanshu Neema, the VU research team seeks to significantly extend these design and simulation capabilities for better understanding the security and resilience aspects of CPS systems. These improvements include first-class support for the design of experiments (exploring different parameters and/or strategies) and target alternative CPS domains (connected vehicles, railway systems, and smart grids), incorporating models of human behavior, and executing multistage games. The researchers also integrate state-of-the-art machine learning libraries and workflows to support security research with AI-assisted CPS applications. They introduced significant changes to the SURE Testbed architecture to achieve these goals, replacing the HLA-based C2 Wind Tunnel federated simulation engine with a more lightweight integration approach within WebGME and DeepForge.
Testbed efforts are focused on developing a fully integrated workflow in DeepForge, targeting the smart grid CPS domain. This work has two major goals: 1) A complete set of prediction, attack, and detection models have been developed for load forecasting applications; and 2) several building blocks of these models--most notably for gradient-based deep neural network attacks--are generalized to form the basis of a future library of reusable components to create SoS experiments involving learning-enabled components. The Testbed development effort is focusing on the initial integration of two existing design studios: (1) DeepForge, a collaborative deep neural network experimentation platform with TensorFlow/Keras backend support and (2) GridLAB-D Design Studio, for configuring and executing smart power grid simulation models through a web-based interface.
The VU team has established a collaborative and technical exchange with the Cybersecurity Research Group at Fujitsu System Integration Laboratories Ltd. This group uses WebGME, DeepForge, and technology elements of the SURE Testbed to develop their Cyber Range product.
A publication based on the research, “Simulation Testbed for Railway Infrastructure Security and Resilience Evaluation,” by Himanshu Neema, Xenofon Koutsoukos, Bradley Potteiger, Cheeyee Tang, and Keith Stouffer won the Best Paper Award at the 7th Annual Symposium on the Science of Security (HoTSoS), held virtually in September 2020.
Additional details on the project can be found here.