Biblio

Filters: Author is Bradley Potteiger  [Clear All Filters]
2018-09-30
Himanshu Neema, Bradley Potteiger, Xenofon Koutsoukos, Gabor Karsai, Peter Volgyesi, Janos Sztipanovits.  2018.  Integrated Simulation Testbed for Security and Resilience of CPS. Proceedings of the 33rd Annual ACM Symposium on Applied Computing. :368–374.

Owing1 to an immense growth of internet-connected and learning-enabled cyber-physical systems (CPSs) [1], several new types of attack vectors have emerged. Analyzing security and resilience of these complex CPSs is difficult as it requires evaluating many subsystems and factors in an integrated manner. Integrated simulation of physical systems and communication network can provide an underlying framework for creating a reusable and configurable testbed for such analyses. Using a model-based integration approach and the IEEE High-Level Architecture (HLA) [2] based distributed simulation software; we have created a testbed for integrated evaluation of large-scale CPS systems. Our tested supports web-based collaborative metamodeling and modeling of CPS system and experiments and a cloud computing environment for executing integrated networked co-simulations. A modular and extensible cyber-attack library enables validating the CPS under a variety of configurable cyber-attacks, such as DDoS and integrity attacks. Hardware-in-the-loop simulation is also supported along with several hardware attacks. Further, a scenario modeling language allows modeling of alternative paths (Courses of Actions) that enables validating CPS under different what-if scenarios as well as conducting cyber-gaming experiments. These capabilities make our testbed well suited for analyzing security and resilience of CPS. In addition, the web-based modeling and cloud-hosted execution infrastructure enables one to exercise the entire testbed using simply a web-browser, with integrated live experimental results display.

Neema, Himanshu, Bradley Potteiger, Xenofon D. Koutsoukos, CheeYee Tang, Keith Stouffer.  2018.  Metrics-Driven Evaluation of Cybersecurity for Critical Railway Infrastructure. IEEE Resilience Week.

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.

2019-05-30
Xenofon Koutsoukos, Gabor Karsai, Aron Laszka, Himanshu Neema, Bradley Potteiger, Peter Volgyesi, Yevgeniy Vorobeychik, Janos Sztipanovits.  2018.  SURE: A Modeling and Simulation Integration Platform for Evaluation of Secure and Resilient Cyber–Physical Systems. Proceedings of the IEEE. 106:93-112.

The exponential growth of information and communication technologies have caused a profound shift in the way humans engineer systems leading to the emergence of closed-loop systems involving strong integration and coordination of physical and cyber components, often referred to as cyber-physical systems (CPSs). Because of these disruptive changes, physical systems can now be attacked through cyberspace and cyberspace can be attacked through physical means. The paper considers security and resilience as system properties emerging from the intersection of system dynamics and the computing architecture. A modeling and simulation integration platform for experimentation and evaluation of resilient CPSs is presented using smart transportation systems as the application domain. Evaluation of resilience is based on attacker-defender games using simulations of sufficient fidelity. The platform integrates 1) realistic models of cyber and physical components and their interactions; 2) cyber attack models that focus on the impact of attacks to CPS behavior and operation; and 3) operational scenarios that can be used for evaluation of cybersecurity risks. Three case studies are presented to demonstrate the advantages of the platform: 1) vulnerability analysis of transportation networks to traffic signal tampering; 2) resilient sensor selection for forecasting traffic flow; and 3) resilient traffic signal control in the presence of denial-of-service attacks.

2019-05-31
Bradley Potteiger, William Emfinger, Himanshu Neema, Xenofon Koutsoukos, CheeYee Tang, Keith Stouffer.  2017.  Evaluating the effects of cyber-attacks on cyber physical systems using a hardware-in-the-loop simulation testbed. Resilience Week (RWS). :177-183.

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.

2017-10-27
Bradley Potteiger, Gonzalo Martins, Xenofon Koutsoukos.  2016.  Software and Attack Centric Integrated Threat Modeling for Quantitative Risk Assessment. 2016 Symposium and Bootcamp on the Science of Security (HotSoS'16).

(No abstract.)

2016-04-11
Aron Laszka, Bradley Potteiger, Yevgeniy Vorobeychik, Saurabh Amin, Xenofon Koutsoukos.  2016.  Vulnerability of Transportation Networks to Traffic-Signal Tampering. 7th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.

2017-10-27
Aron Laszka, Bradley Potteiger, Yevgeniy Vorobeychik, Saurabh Amin, Xenofon Koutsoukos.  2016.  Vulnerability of Transportation Networks to Traffic-Signal Tampering. 7th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.