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Today’s high-stakes adversarial interactions feature attackers who constantly breach the ever-improving security measures. Deception mitigates the defender’s loss by misleading the attacker to make suboptimal decisions. In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary’s preferences which are initially unknown to the defender. We make the following contributions. (1) We show that we can uniformly learn the adversary’s preferences using data from a modest number of deception strategies. (2) We propose an approximation algorithm for finding the optimal deception strategy given the learned preferences and show that the problem is NP-hard. (3) We perform extensive experiments to validate our methods and results. In addition, we provide a case study of the credit bureau network to illustrate how FDP implements deception on a real-world problem.

Distributed denial-of-service attacks are an increasing problem facing web applications, for which many defense techniques have been proposed, including several moving-target strategies. These strategies typically work by relocating targeted services over time, increasing uncertainty for the attacker, while trying not to disrupt legitimate users or incur excessive costs. Prior work has not shown, however, whether and how a rational defender would choose a moving-target method against an adaptive attacker, and under what conditions. We formulate a denial-of-service scenario as a two-player game, and solve a restricted-strategy version of the game using the methods of empirical game-theoretic analysis. Using agent-based simulation, we evaluate the performance of strategies from prior literature under a variety of attacks and environmental conditions. We find evidence for the strategic stability of various proposed strategies, such as proactive server movement, delayed attack timing, and suspected insider blocking, along with guidelines for when each is likely to be most effective.