Publications

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Critical infrastructure systems continue to foster predictable communication patterns and static configurations over extended periods of time. The static nature of these systems eases the process of gathering reconnaissance information that can be used to design, develop, and launch attacks by adversaries. In this research effort, the early phases of an attack vector will be disrupted by randomizing application port numbers, IP addresses, and communication paths dynamically through the use of overlay networks within Industrial Control Systems (ICS). These protective measures convert static systems into "moving targets," adding an additional layer of defense. Additionally, we have developed a framework that automatically detects and defends against threats within these systems using an ensemble of machine learning algorithms that classify and categorize abnormal behavior. Our proof-of-concept has been demonstrated within a representative ICS environment. Performance metrics of our proof-of-concept have been captured with latency impacts of less than a millisecond, on average.

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Moving target defense (MTD) is an emerging paradigm in which system defenses dynamically mutate in order to decrease the overall system attack surface. Though the concept is promising, implementations have not been widely adopted. The field has been actively researched for over ten years, and has only produced a small amount of extensively adopted defenses, most notably, address space layout randomization (ASLR). This is despite the fact that there currently exist a variety of moving target implementations and proofs-of-concept. We suspect that this results from the moving target controls breaking critical system dependencies from the perspectives of users and administrators, as well as making things more difficult for attackers. As a result, the impact of the controls on overall system security is not sufficient to overcome the inconvenience imposed on legitimate system users. In this paper, we analyze a successful MTD approach. We study the control's dependency graphs, showing how we use graph theoretic and network properties to predict the effectiveness of the selected control.

Moving target defense (MTD) is an emerging paradigm in which system defenses dynamically mu- tate in order to decrease the overall system attack surface. Though the initial concept is promising, implementations have not been widely adopted. The field has been actively researched for over ten years, and has only produced a small amount of extensively adopted defenses, most notably, address space layout randomization (ASLR). This is despite the fact that there currently exist a variety of moving target implementations and proofs-of-concept. We suspect that this results from the moving target controls breaking critical system dependencies from the perspectives of users and administrators, as well as making things more difficult for attackers. As a result, the impact of the controls on overall system security is not sufficient to overcome the inconvenience imposed on legitimate system users. In this paper, we analyze a successful MTD approach. We study the con- trol's dependency graphs, showing how we use graph theoretic and network properties to predict the effectiveness of the selected control. Then, with this framework in place, the dynamic nature of some Moving Target Defenses opens the possibility of modeling them with dynamic systems approaches, such as state space representations familiar from control and systems theory. We then use this approach to develop state space models for Moving Target Defenses, provide an analysis of their properties, and suggest approaches for using them.

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Today's globalized supply chains are complex systems of systems characterized by a conglomeration of interconnected networks and dependencies. There is a constant supply and demand for materials and information exchange with many entities such as people, organizations, processes, services, products, and infrastructure at various levels of involvement. Fully comprehending supply chain risk (SCR) is a challenging problem, as attacks can be initiated at any point within the system lifecycle and can have detrimental effects to mission assurance. Counterfeit items, from individual components to entire systems, have been found in commercial and government systems. Cyber-attacks have been enabled by suppliers' lack of security. Furthermore, there have been recent trends to incorporate supply chain security to help defend against potential cyber-attacks, however, we find that traditional supply chain risk reduction and screening methods do not typically identify intrinsic vulnerabilities of realized systems. This paper presents the application of a supply chain decision analytics framework for assisting decision makers in performing risk-based cost-benefit prioritization of security investments to manage SCR. It also presents results from a case study along with discussions on data collection and pragmatic insight to supply chain security approaches. This case study considers application of the framework in analyzing the supply chain of a United States Government critical infrastructure construction project, clarifies gaps between supply chain analysis and technical vulnerability analysis, and illustrates how the framework can be used to identify supply chain threats and to suggest mitigations.

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Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and development to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.

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Physical unclonable functions (PUFs) make use of the measurable intrinsic randomness of physical systems to establish signatures for those systems. PUFs provide a means to generate unique keys that don't need to be stored in nonvolatile memory, and they offer exciting opportunities for new authentication and supply chain security technologies.

Physical unclonable functions (PUFs) make use of the measurable intrinsic randomness of physical systems to establish signatures for those systems. Thus, PUFs provide a means to generate unique keys that don't need to be stored in nonvolatile memory, and they offer exciting opportunities for new authentication and supply chain security technologies.