Alexander V. Outkin

Decision framework for evaluating the macroeconomic risks and policy impacts of cyber attacks

Abstract Increased reliance on the Internet for critical infrastructure and the global nature of supply chains provides an opportunity for adversaries to leverage dependencies and gain access to vital infrastructure. Traditional approaches to assessing risk in the cyber domain, including estimation of impacts, fall short due to uncertainty in how interconnected systems react to cyber attack. This paper describes a method to represent the pathways of disruption propagation, evaluate the macroeconomic impact of cyber threats and aid in selecting among various cybersecurity policies. Based on state of the art agent-based modeling, multicriteria decision analysis, and macroeconomic modeling tools, this framework provides dynamic macroeconomic, demographic and fiscal insights regarding shocks caused by cyber attacks to the regional economy over time. The interlinkage of these models will provide a robust and adaptive system that allows policy makers to evaluate complex issues such as cybersecurity threats and their impacts on the geopolitical, social, environmental, and macroeconomic landscape.

Evaluating Moving Target Defense with PLADD

This project evaluates the effectiveness of moving target defense (MTD) techniques using a new game we have designed, called PLADD, inspired by the game FlipIt [28]. PLADD extends FlipIt by incorporating what we believe are key MTD concepts. We have analyzed PLADD and proven the existence of a defender strategy that pushes a rational attacker out of the game, demonstrated how limited the strategies available to an attacker are in PLADD, and derived analytic expressions for the expected utility of the game’s players in multiple game variants. We have created an algorithm for finding a defender’s optimal PLADD strategy. We show that in the special case of achieving deterrence in PLADD, MTD is not always cost effective and that its optimal deployment may shift abruptly from not using MTD at all to using it as aggressively as possible. We believe our effort provides basic, fundamental insights into the use of MTD, but conclude that a truly practical analysis requires model selection and calibration based on real scenarios and empirical data. We propose several avenues for further inquiry, including (1) agents with adaptive capabilities more reflective of real world adversaries, (2) the presence of multiple, heterogeneous adversaries, (3) computational game theory-based approaches such as coevolution to allow scaling to the real world beyond the limitations of analytical analysis and classical game theory, (4) mapping the game to real-world scenarios, (5) taking player risk into account when designing a strategy (in addition to expected payoff), (6) improving our understanding of the dynamic nature of MTD-inspired games by using a martingale representation, defensive forecasting, and techniques from signal processing, and (7) using adversarial games to develop inherently resilient cyber systems.

Creating interaction environments: defining a two-sided market model of the development and dominance of platforms

Interactions between individuals, both economic and social, are increasingly mediated by technological systems. Such platforms facilitate interactions by controlling and regularizing access, while extracting rent from users. The relatively recent idea of two-sided markets has given insights into the distinctive economic features of such arrangements, arising from network effects and the power of the platform operator. Simplifications required to obtain analytical results, while leading to basic understanding, prevent us from posing many important questions. For example we would like to understand how platforms can be secured when the costs and benefits of security differ greatly across users and operators, and when the vulnerabilities of particular designs may only be revealed after they are in wide use. We define an agent-based model that removes many constraints limiting existing analyses (such as uniformity of users, free and perfect information), allowing insights into a much larger class of real systems.

Input-output model for MACCS nuclear accident impacts estimation¹

Since the original economic model for MACCS was developed, better quality economic data (as well as the tools to gather and process it) and better computational capabilities have become available. The update of the economic impacts component of the MACCS legacy model will provide improved estimates of business disruptions through the use of Input-Output based economic impact estimation. This paper presents an updated MACCS model, bases on Input-Output methodology, in which economic impacts are calculated using the Regional Economic Accounting analysis tool (REAcct) created at Sandia National Laboratories. This new GDP-based model allows quick and consistent estimation of gross domestic product (GDP) losses due to nuclear power plant accidents. This paper outlines the steps taken to combine the REAcct Input-Output-based model with the MACCS code, describes the GDP loss calculation, and discusses the parameters and modeling assumptions necessary for the estimation of long-term effects of nuclear power plant accidents.