FY2015 Annual Report. Towards an Economic Behavioral Science Approach to Cyber Security. Scott Farrow UMBC,

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1 FY2015 Annual Report Towards an Economic Behavioral Science Approach to Cyber Security Scott Farrow UMBC, Contents 1. Executive Summary Research and Research Transition Accomplishments Research Results Research Transition Publications, Reports and Presentations Models, Databases, Software Tools, Invention Disclosures and Patents Education Programs Outreach Programs for this project Project Performance Metrics Executive Summary This project had two major tasks: 1) extending detail to inform the choice between major decision-support methods such as decision-analysis and benefit-cost analysis, and 2) evolution of micro-economic models for cyber-security investments. Results of the decision-support modeling work have led to clearer logic flows for risk based decision-making, in particular how differing assumptions made by analysts or decision-makers lead to differing tools with extended material on the incorporation of risk into benefit-cost analysis. Results for cyber-security have extended the well-known cyber-security investment model of Gordon and Leob and provided more complete guidance for cyber security investments including external effects, risk inter-dependencies and advanced persistent threats. Below are two key figures from the supported work, the first illustrating a flow of analytical decisions leading to a particular type of decision-analysis which was extended in the work, and the other some extensions of a popular economic model of cyber-security investment McClintock Avenue, RTH314 ~ Los Angeles, CA ~ (213) ~ create.usc.edu

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3 2. Research and Research Transition Accomplishments 2.1. Research Results Summary of conclusions for the choice of decision-support models via meta-model choice The models chosen were based on experience in cross agency practices and academic experience in engineering, public policy, and economics. Other researchers add additional models. The models considered here are: Impact analysis as exemplified by a National Environmental Policy Act type of analysis Standard Decision Analysis as exemplified by expected utility or an expected outcome analysis Advanced Decision Analysis as exemplified by a multi-criteria/multi attribute analysis Standard Benefit-Cost Analysis as exemplified by equal utility weighting and expected values Advanced Benefit-Cost Analysis as exemplified by equity weighting and information on statistical outcomes Economic consequence analysis as exemplified by input-output or computable general equilibrium analysis Cost Effectiveness Analysis as exemplified by the least cost to achieve a stated objective While the lines between some of the analyses can be blurred, the attempt below is to identify those assumptions, as standardly used, which distinguish the risk management alternative framings. While additional detail is available in a submitted chapter, areas in which the models were consistent across models as generally applied (exceptions may always exist), included: 1) Ability to define who has standing, 2) That individual preferences were generally modeled as independent on one another, The distinguishing characteristics among the models, some of which are bundled together in the discussion to follow for ease of presentation, are: a) whose preferences are being modeled, b) the goal and associated metric, c) the method of aggregation, including weights if relevant, and d) risk measures and preferences. Each is discussed briefly below with a focus on those models with the most divergent approaches. 1. Whose ultimate preferences are modeled? This assumption distinguishes most forms of decision analysis from benefit-cost analysis. In decision analysis, the analyst is typically modeling the preferences of one or a few people who are generally considered to be taking the role of a decision-maker, stakeholder, or representative consumer In contrast, benefit-cost analysis models the preferences of those in society who have standing. The question of whose preferences are being modeled is ambiguous for a NEPA type impact analysis. 2. The goal and associated metrics. The goal or objective function of some model types is clear while others are more ambiguous. A NEPA type analysis has no unifying objective function and reports metrics in their natural units (illnesses, crimes, health outcomes, Page 3 of 8

4 dollars). Impact analysis typically reports Gross Domestic Product but also dimensions such as unemployment without a unifying objective. Decision-analysis typically uses monetary or utility measures for the outcomes with the goal the maximization of the common metric. The outcomes may be decomposed into attributes. Benefit-cost analysis aspires to use surplus measures (measures of value taking into account different valuation for the infra-marginal units) but standard benefit-cost analysis typically applies an average value which may or may not be based on concepts of surplus. The goal is to maximize total surplus (net benefits) which economists define as (economic) efficiency. Advanced benefit-cost analyses often take into account measures of surplus possibly including option price or other variations involving risk preferences. Regarding descriptive or other statistics used, the expected value of the preferred measure is typically reported although increasingly, more information about the statistical distribution of the risk metric is presented. 3. Method of aggregation, including aggregation weights: There are often two dimensions of aggregation: 1) for one individual, aggregating across impacts if multiple impacts exist or 2) aggregating across individuals. Here the assumption is that social impacts, broadly based, are the object of estimation in contrast to impacts on a select group such as a select group of consumers, firms, or the government alone. In an impact analysis, individual impacts such as new jobs are typically aggregated equally across individuals for impacts in a particular category but no attempt is made to aggregate across impact categories, as across jobs and environmental impacts. Economic impact, or consequence analyses, are defined in monetary terms and typically aggregate equally within and across economic impact categories. With decision analysis, numerous means exist to develop weights to aggregate impacts for one individual but the standard application is without aggregation of individual preferences. Standard benefit-cost analysis uses monetary values to aggregate both for an individual and across individuals. The latter typically assumes an equal social and individual value per incremental dollar to each member of society. Advanced benefit-cost analyses will sometimes weight dollar impacts to different groups in the aggregation process, often though not necessarily based on different income groups 4. Risk Preferences: Risk preferences are here taken to be any of risk neutral, risk loving, risk averse, or some behavioral risk approach (Eeckhoudt et al., 2005). Such preferences can be revealed at two stages of an analysis. The first are the risk preferences of individuals and the second are the risk preferences in any social aggregation. Impact analysis typically ignores such risk preferences. Some advanced types of economic consequences analysis may include some behavioral risk effect, but the majority of economic impact analyses assume a risk neutral approach on the part of both individuals and in the aggregation process. Consequently the standard risk metric is an expected value impact, often an expenditure or income. Basic decision analysis often assumes risk neutrality by analyzing the expected value of a decision. Advanced decision analyses can consider alternative risk preferences as measured by utility, and then may aggregate using expected or a non-expected utility approach. Standard benefit-cost analysis is carried out in the world of certainty for individual outcomes and the aggregation of individual values is based on the risk neutrality, or expected value, of both the individual and any Governmental decision-maker. Advanced benefit-cost analyses Page 4 of 8

5 may take into account risk preferences in assessing the valuation of individuals, and then typically although not necessarily present the expected value of such valuations (Arrow and Lind, 1970; Keeney and Raiffa, 1976; Haimes, 2005). An assumption which tends to but need not vary across model types includes the treatment of impacts in different time period which is usually aggregated into a present value metric by discounting. Benefit-Cost analysis routinely uses discounting of future values or annualization of values as do many decision-analyses. Discounting may be used less commonly in other analyses. Summary of conclusions from work on the micro-economic modeling of cyber-security Gordon and Leob (GL) along with co-authors have developed an influential model of cybersecurity investment with important implications on the size of cyber-security investment. The key issues and extensions to the Gordon and Leob model developed during the project include: Consideration of externalities is a primary concern for government policy. Although GL imply that externalities are unlikely to be large; evidence developed by the project indicates that externalities may not be internalized through the legal process in the private sector and that externalities can be significant even for identity theft. This implies significantly larger expenditures than those based on internalized, private sector damages. While an expected value decision-maker will not spend more than expected losses, the empirical functional form affects how much less, if any, than the expected value of damages is invested. Prior assumptions may have been unduly restrictive such that a larger fraction of expected loss can be appropriate. When investment considerations include multiple datasets or sites, public defenses that protect many sites, an ability to invest in reducing damages, and several other extensions then the optimal (constrained) level of cyber investment changes and requires economic security equivalency across sites and data-sets to the extent technologically possible. When additional modeling of uncertainty is added, whether in the form of irreversible losses or risk aversion; it is typical (although not required) to develop models in which cyber-security investments exceed expected losses. Current is also detailing more carefully the modeling of cyber-security Losses as used in common cyber-security models Research Transition The work on the meta-model choice of decision-support models was inspired by issues faced by groups such as an executive risk committee at DHS whose task involves comparing and coordinating risks across the DHS enterprise. It was thought that a structure that compares multiple types of risk can help decision-makers choose that which is most relevant to them, and Page 5 of 8

6 to avoid capture the analysts who unsurprisingly often have a preferred analytic methods with which they are familiar. The work on cyber-security has applied relevance both for protecting the government cyberenterprise but also for its interaction with the private sector. Some existing models popular in the public domain may under-state some of the importance of cyber-security on a societal basis. Additional insight was obtained through some days embedded with DHS Cyber Security and Communication office within the National Protection and Programs Directorate Publications, Reports and Presentations Working papers for publication Farrow, S., Benefit-Cost Analysis and Risk, under review for inclusion in Improving Homeland Security Decisions, ed. Abbas, A. and D. Von Winterfelt. Farrow, S. and J. Szanton, Cyber Security Investment Guidance: A Note on Extensions of the Gordon and Leob model, possible submission for special issue of the Journal of Information Security focused on security investments. Presentations Farrow, S., Meta-Model Choice, Association of Public Policy Analysis and Management, Albuquerque, NM. Farrow, S., Meta-Model Choice, The Benefit-Cost Society, Washington, D.C. Farrow, S., Challenges with Risk, invited paper for presentation at the University of Toulouse, France. Farrow, S., Meta-Model Choice and the Economic Modeling of Cyber-Security Investments, presented at USC/CREATE Models, Databases, Software Tools, Invention Disclosures and Patents Models intended for use or transition to DHS enterprise 1. Logic models for choice of decision-support tools. 2. Optimization (non-numerical) models for cyber-security investments. 3. Education Programs Page 6 of 8

7 The cyber security research incorporated a module on the legal liability for different types of cyber-attacks. The project coordinated with the University of Baltimore (a separate campus in the University of Maryland System), Center for Health and Homeland Security and incorporated the research of a third year law student. Student: Jules Szanton Campus: University of Maryland, Baltimore; Carey School of Law 4. Outreach Programs for this project Meetings were held with: 1) Tony Cheesebrough, NPPD. 2) Matt Shabat, Computer Security and Communications, NPPD 3) Debra Elkins and Joseph Simmons, SPAR 5. Project Performance Metrics Table 1: FY2015 Project Performance Metrics Categories of Accomplishments Number of: FY2015 (Year 11) Student Enrollment in COE Programs: 0 Traditional undergraduate and graduate students attending classes 0 Students registered in on-line or other distance learning courses 0 Homeland security professionals attending courses 0 Project-Funded Students 1 Papers 2 Software Products Developed 0 New Courses Developed 0 New Certificates or Degree Programs Developed: 0 New Degree Programs 0 New Certificates 0 New private or public licensees/partners using coursework licensed by the COE 0 Patent Applications 0 Patents Awarded 0 Requests for assistance or advice from DHS (# of different DHS contacts/projects/requests) 0 Requests for assistance or advice from Federal, State, Local Government 0 Follow-on funding from other sources 0 Presentations 4 Congressional Testimonies 0 Projects Completed 0 Page 7 of 8

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