Network Inspection Using Heterogeneous Sensors for Detecting Strategic Attacks

We consider a two-player network inspection game, in which a defender positions heterogeneous sensors according to a probability distribution in order to detect multiple attacks caused by a strategic attacker. We assume the defender has access to multiple types of sensors that can potentially differ in their accuracy. The objective of the defender (resp. attacker) is to minimize (resp. maximize) the expected number of undetected attacks. We derive a Nash equilibrium of this zero-sum game under the assumption that each component in the network can be monitored from a unique sensor location. We then leverage our constructed Nash equilibrium to provide approximate solutions to the general case by solving a minimum set cover problem. Our results illustrate the performance and computational advantage of our solution approach, as well as the value of strategically leveraging heterogeneous sensors to protect critical networks against attacks

Dynamic network analysis of a target defense differential game with limited observations

In this paper, we study a Target-Attacker-Defender (TAD) differential game involving one attacker, one target and multiple defenders. We consider two variations where (a) the attacker and the target have unlimited observation range and the defenders are visibility constrained (b) only the attacker has unlimited observation range and the remaining players are visibility constrained. We model the players' interactions as a dynamic game with asymmetric information. Here, the visibility constraints of the players induce a visibility network which encapsulates the visibility information during the evolution of the game. Based on this observation, we introduce network adapted feedback or implementable strategies for visibility constrained players. Using inverse game theory approach we obtain network adapted feedback Nash equilibrium strategies. We introduce a consistency criterion for selecting a subset (or refinement) of network adapted feedback Nash strategies, and provide an optimization based approach for computing them. Finally, we illustrate our results with numerical experiments.Comment: 8 figure

Focality and Asymmetry in Multi-battle Contests

This article examines behavior in two-person constant-sum Colonel Blotto games in which each player maximizes the expected total value of the battlefields won. A lottery contest success function is employed in each battlefield. Recent experimental research on such games provides only partial support for Nash equilibrium behavior. We hypothesize that the salience of battlefields affects strategic behavior (the salient target hypothesis). We present a controlled test of this hypothesis – against Nash predictions – when the sources of salience come from certain asymmetries in either battlefield values or labels (as in Schelling (1960)). In both cases, subjects over-allocate the resource to the salient battlefields relative to the Nash prediction. However, the effect is stronger with salient values. In the absence of salience, we replicate previous results in the literature supporting the Nash prediction

Focality and Asymmetry in Multi-battle Contests

This article examines the influence of focality in Colonel Blotto games with a lottery contest success function (CSF), where the equilibrium is unique and in pure strategies. We hypothesise that the salience of battlefields affects strategic behaviour (the salient target hypothesis) and present a controlled test of this hypothesis against Nash predictions, checking the robustness of equilibrium play. When the sources of salience come from asymmetries in battlefield values or labels (as in Schelling, 1960), subjects over-allocate the resource to the salient battlefields relative to the Nash prediction. However, the effect is stronger with salient values. In the absence of salience, we find support for the Nash prediction