In this paper we present a mixed qualitative and quantitative approach for evaluation of Information Technology (IT) security investments.For this purpose, we model security scenarios by using defense trees, an extension of attack trees with attack countermeasures and we use economic quantitative indexes for computing the defender's return on security investment and the attacker's return on attack.We show how our approach can be used to evaluate effectiveness and economic profitability of countermeasures as well as their deterrent effect on attackers, thus providing decision makers with a useful tool for performing better evaluation of IT security investments during the risk management process.

Abstract. In this paper we use defense trees, an extension of attack trees with countermeasures, to represent attack scenarios and game theory to detect the most promising actions attacker and defender. On one side the attacker wants to break the system (with as little efforts as possible), on the opposite side the defender want to protect it (sustaining the minimum cost). As utility function for the attacker and for the defender we consider economic indexes (like the Return on Investment (ROI) and the Return on Attack (ROA)). We show how our approach can be used to evaluate effectiveness and economic profitability of countermeasures as well as their deterrent effect on attackers, thus providing decision makers with a useful tool for performing better evaluation of IT security investments during the risk management process.

In this paper we present a qualitative approach for the selection of security countermeasures able to protect an IT system from attacks. For this purpose, we model security scenarios by using defense trees (an extension of attack trees) and preferences over countermeasure using Conditional Preference networks (CP-nets for short). In particular, we introduce two different methods for the composition of preferences: the and-composition and the or-composition. The first one is used to determine a preference order in the selection of countermeasures able to mitigate the risks produced by conjunct attacks. The second one is used to determine a preference order over sets of countermeasures able to mitigate the risks produced by alternative attacks.

Defence trees are used to represent attack and defence strategies in security scenarios; the aim in such scenarios is to select the best set of countermeasures that are able to stop all the vulnerabilities. In order to represent preferences among possible countermeasures of a given attack, defence trees are enriched with conditional preferences, obtaining a new structure called CP-defence tree. In this paper we transform a CP-defence tree with preferences among attacks and countermeasures in an Answer Set Optimization (ASO) program. The ASO program, representing the overall scenario, is a special composition of the programs associated to each branch of a CP-defence tree. We describe an implementation that select the best set of countermeasure able to mitigate all the vulnerabilities by computing the optimal answer set of the corresponding ASO program.

In this article, we present a mixed qualitative and quantitative approach for evaluation of information technology (IT) security investments. For this purpose, we model security scenarios by using defense trees, an extension of attack trees with countermeasures and we use economic quantitative indexes for computing the defender's return on security investment and the attacker's return on attack. We show how our approach can be used to evaluate economic profitability of countermeasures and their deterrent effect on attackers, thus providing decision makers with a useful tool for performing better evaluation of IT security investments during the risk management process.