Optimal voting strategy against rational attackers

Wang, Li; Li, Zheng; Ren, Shangping; Kwiaty, Kevin

doi:10.1109/crisis.2011.6061841

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…All attacked unprotected units are compromised. The probability that out of attacked protected units are compromised is (18) If protected units and unprotected units are compromised, the total number of compromised units is .…”

Section: Distribution Of Number Of Compromised Voting Unitsmentioning

confidence: 99%

“…The probability that exactly units are compromised given units are protected and units are attacked is (19) where is determined using (18). The probability of TVS success given units are protected and units are attacked is (20) IV.…”

Section: Asmentioning

confidence: 99%

“…Examples of such attacks are virus attacks in computer systems, jamming of radio location stations of anti-aircraft defense systems, camouflaging targets in battle conditions, etc. Only a few works were devoted to the analysis of voting systems operating in hostile conditions: [16] considers the reliability of weighted TVS with limited availability of voting units that can be totally destroyed by a corrosive medium or intentional attacks; [17] analyzes voting unit separation as a measure of TVS defense against an attack; [18] first assesses strategic behavior of the attacker that chooses the optimal number of voting systems to attack, and analyzes the defender's choice of units participating in the voting. This work considers a simple majority voting system, and assumes that the attacker compromises a fixed number of units, and that this number does not depend on the units' protection and the attack intensity.…”

mentioning

confidence: 99%

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Defending Threshold Voting Systems With Identical Voting Units

Levitin

Hausken

2013

IEEE Trans. Rel.

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The threshold voting system consists of units that each provide a binary decision (0 or 1), or abstain from voting. System output is 1 if the number of 1-opting units is at least a pre-specified fraction of the number of all non-abstaining units. Otherwise system output is 0. For a system consisting of voting units with given probabilistic output distribution, one can maximize the entire system reliability by choosing a proper threshold value . When a system operates in a hostile environment, some units can be destroyed or compromised by an aggressive media, or by a strategic malicious counterpart. One of the ways to enhance voting system survivability is to protect its units from possible attacks. We consider a situation when an attacker and a defender have fixed resources. The defender can protect, and the attacker can attack, a subset of the units. First, we formulate the problem of maximizing survivability of a threshold voting system by proper choice of system threshold and number of protected units, assuming that all the units are attacked. Then we consider a maxmin game in which the defender chooses an optimal system threshold and number of protected units assuming that the attacker chooses the number of attacked units that minimizes the probability of correct system output.

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Section: Distribution Of Number Of Compromised Voting Unitsmentioning

confidence: 99%

Section: Asmentioning

confidence: 99%

mentioning

confidence: 99%

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Defending Threshold Voting Systems With Identical Voting Units

Levitin

Hausken

2013

IEEE Trans. Rel.

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“…Following the previous research, Wang et al [12] analyzed the nodes decision in a cluster. There are n clusters of m nodes, with a total of n*m nodes.…”

Section: Related Workmentioning

confidence: 99%

Resilient Voting Mechanisms for Mission Survivability in Cyberspace: Combining Replication and Diversity

Kamhoua¹,

Hurley²,

Kwiat³

et al. 2012

IJNSA

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“…The authors showed how an attacker can infiltrate a network at one node, and from the compromised node, the attacker can breach easily and access other nodes of the network infrastructure. Wang et al [81] analyzed the nodes' decision in a cluster environment. The cluster environment is composed of n clusters of m nodes, which is n x m individual nodes.…”

Section: Contributionmentioning

confidence: 99%

Modeling Security and Resource Allocation for Mobile Multi-hop Wireless Neworks Using Game Theory

Njilla¹

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Optimal voting strategy against rational attackers

Cited by 13 publications

References 30 publications

Defending Threshold Voting Systems With Identical Voting Units

Defending Threshold Voting Systems With Identical Voting Units

Resilient Voting Mechanisms for Mission Survivability in Cyberspace: Combining Replication and Diversity

Modeling Security and Resource Allocation for Mobile Multi-hop Wireless Neworks Using Game Theory

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