Simulation and Game-Theoretic Analysis of an Attacker-Defender Game

Nochenson, Alan; Heimann, Candice

doi:10.1007/978-3-642-34266-0_8

Cited by 13 publications

(11 citation statements)

References 18 publications

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“…Then these inputs and submitted 6 to the KMS appropriately by the GCU 7 . The information is analyzed and attack/attacks is/are identified using attack identification system.…”

Section: Operational Flowmentioning

confidence: 99%

“…Not only different attacks warrant for different games to be played with different appropriate game models, different scenarios with same kind of attack may need different game models with 6 The GIDA Action Agent uses the first lookup table for this. 7 A typical example for this would be when two different sensors detect slight anomalies, but individual neither large enough to warrant a standard reflex action, as per sensor/actuator policies.…”

Section: Game Model Repositorymentioning

confidence: 99%

“…( ) 8,6 is the prescribed  -NOSP. It recommends for the user to be less aggressive as he is more affective while the administrator to be quite aggressive.…”

Section: Average User (Au)mentioning

confidence: 99%

“…We make two extensions to this work. The simulation method used to study and explore the value of the game and the rewards for the strategies are an extension of [6] which has only 5 strategic options.…”

Section: Introductionmentioning

confidence: 99%

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On a Generic Security Game Model

Shandilya¹,

Shiva²

2017

IJCNS

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To protect the systems exposed to the Internet against attacks, a security system with the capability to engage with the attacker is needed. There have been attempts to model the engagement/interactions between users, both benign and malicious, and network administrators as games. Building on such works, we present a game model which is generic enough to capture various modes of such interactions. The model facilitates stochastic games with imperfect information. The information is imperfect due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over other multiple states, we use Euclidean distances between the outputs of the sensors. We build a 5-state game to represent the interaction of the administrator with the user. The states correspond to 1) the user being out of the system in the Internet, and after logging in to the system; 2) having low privileges; 3) having high privileges; 4) when he successfully attacks and 5) gets trapped in a honeypot by the administrator. Each state has its own action set. We present the game with a distinct perceived action set corresponding to each distinct information set of these states. The model facilitates stochastic games with imperfect information. The imperfect information is due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over the states, we use Euclidean distances between outputs of the sensors. A numerical simulation of an example game is presented to show the evaluation of rewards to the players and the preferred strategies. We also present the conditions for formulating the strategies when dealing with more than one attacker and making collaborations.

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“…Then these inputs and submitted 6 to the KMS appropriately by the GCU 7 . The information is analyzed and attack/attacks is/are identified using attack identification system.…”

Section: Operational Flowmentioning

confidence: 99%

Section: Game Model Repositorymentioning

confidence: 99%

“…( ) 8,6 is the prescribed  -NOSP. It recommends for the user to be less aggressive as he is more affective while the administrator to be quite aggressive.…”

Section: Average User (Au)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On a Generic Security Game Model

Shandilya¹,

Shiva²

2017

IJCNS

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“…In a transportation setting, path-planning against adversaries was formulated as a distributed resource allocation problem and a linear-programming solution was proposed [8]. Finally, agent-based simulations are used in [9] to determine Nash equilibrium strategies in a network security game where both attacker and defender are subject to cost constraints.…”

Section: Introductionmentioning

confidence: 99%

Optimal defensive resource allocation for a centrality-based security game on multi-hop networks

Riehl

Cao

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We present a new analysis of multi-hop network security in the form of a zero-sum game played between an attacker who tries to disrupt a network by disabling one or more nodes, and the nodes of the network who must allocate limited resources in defense of the network. The payoffs in the zero-sum game can be one of several performance metrics that correspond to node centrality measures. In the case of single-node attacks, we use a monotonicity property of the mixed attack strategies to construct a simple and very fast algorithm to compute saddle-point equilibrium strategies for both single-node and multiple-node defense. For simultaneous multiple-node attacks on large networks, the computational complexity becomes quite high, so we present a method to approximate the equilibrium strategies based on a sequential simplification, which performs well in simulations.

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