Optimal Placement of Honeypots for Network Defense

Bilinski, Mark; Gabrys, Ryan; Mauger, Justin

doi:10.1007/978-3-030-01554-1_7

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…Finally, honeypots could be applicable in smart buildings to emulate smart appliances and energy meters or in Advanced Metering Infrastructures (AMIs), operated by DSOs, to emulate smart meters [ 24 ]. It is assumed that the defender has a fixed set of resources and, as a result, is only able to defend a limited number of hosts [ 21 ].…”

Section: System Modelmentioning

confidence: 99%

“…Finally, Bilinski et al in [ 21 ] investigated the Nash Equilibrium (NE) of a honeynet system, in which the defender aims to protect a number of network hosts and has a fixed set of resources, therefore can defend only a limited number of hosts. On the contrary, the attacker can attack a specific number of hosts concurrently, although no cost is incurred to the adversary for each attack.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, a similar scenario has been investigated in [ 22 ], assuming though that the defender has complete information about the attacker’s payoff, a cost function for the attacker, and fixed number of served real devices, which has led to the formulation of a Stackelberg game. Moreover, in contrast in [ 21 ], the payoff of the attacker has been considered to be an increasing function of the number of attacked real devices.…”

Section: Introductionmentioning

confidence: 99%

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Game Theoretic Honeypot Deployment in Smart Grid

Diamantoulakis

Dalamagkas

Radoglou-Grammatikis

et al. 2020

Sensors

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The smart grid provides advanced functionalities, including real-time monitoring, dynamic energy management, advanced pricing mechanisms, and self-healing, by enabling the two-way flow of power and data, as well as the use of Internet of Things (IoT) technologies and devices. However, converting the traditional power grids to smart grids poses severe security challenges and makes their components and services prone to cyber attacks. To this end, advanced techniques are required to mitigate the impact of the potential attacks. In this paper, we investigate the use of honeypots, which are considered to mimic the common services of the smart grid and are able to detect unauthorized accesses, collect evidence, and help hide the real devices. More specifically, the interaction of an attacker and a defender is considered, who both optimize the number of attacks and the defending system configuration, i.e., the number of real devices and honeypots, respectively, with the aim to maximize their individual payoffs. To solve this problem, game theoretic tools are used, considering an one-shot game and a repeated game with uncertainty about the payoff of the attacker, where the Nash Equilibrium (NE) and the Bayesian NE are derived, respectively. Finally, simulation results are provided, which illustrate the effectiveness of the proposed framework.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Game Theoretic Honeypot Deployment in Smart Grid

Diamantoulakis

Dalamagkas

Radoglou-Grammatikis

et al. 2020

Sensors

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“…For repeated games, the value of suboptimal play is illustrated in (Bilinski, Gabrys, and Mauger 2018) by mathematically showing the disadvantage that defenders suffer when using a rational greedy strategy. We argue that by using cyber deception, the defender can create an illusion of playing suboptimally, which presents an opportunity to shift the advantage away from an attacker.…”

Section: The Value Of Suboptimal Playmentioning

confidence: 99%

Artificial Intelligence and Game Theory Models for Defending Critical Networks with Cyber Deception

Fugate

Ferguson-Walter

2019

AI Magazine

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Traditional cyber security techniques have led to an asymmetric disadvantage for defenders. The defender must detect all possible threats at all times from all attackers and defend all systems against all possible exploitation. In contrast, an attacker needs only to find a single path to the defender’s critical information. In this article, we discuss how this asymmetry can be rebalanced using cyber deception to change the attacker’s perception of the network environment, and lead attackers to false beliefs about which systems contain critical information or are critical to a defender’s computing infrastructure. We introduce game theory concepts and models to represent and reason over the use of cyber deception by the defender and the effect it has on attacker perception. Finally, we discuss techniques for combining artificial intelligence algorithms with game theory models to estimate hidden states of the attacker using feedback through payoffs to learn how best to defend the system using cyber deception. It is our opinion that adaptive cyber deception is a necessary component of future information systems and networks. The techniques we present can simultaneously decrease the risks and impacts suffered by defenders and dramatically increase the costs and risks of detection for attackers. Such techniques are likely to play a pivotal role in defending national and international security concerns.

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“…Prior research shows that choosing the optimal number of honeypots in the network can help deceive the attacker into the trap (Píbil et al, 2012). Similarly, Bilinski et al (2018) presented a honeynet game to assign honeypots in the network in the most efficient way to ensure high-valued resources. However, further research is needed to see how varied honeypot proportions impact adversarial decision-making in cyberspace.…”

Section: Introductionmentioning

confidence: 99%

Influence of different honeypot proportions on adversarial decisions in a deception game

Katakwar

Uttrani

Aggarwal

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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Cyberattacks are proliferating, and deception via honeypots may provide efficient strategies for combating cyberattacks. Although prior research has examined deception and network factors using deception-based games, it is still unknown how the proportion of honeypots in a network influences the adversarial decision. This study evaluates the influence of different honeypot proportions on the adversary’s decisions using a deception game (DG). DG has two consecutive stages, probe and attack. In the probe stage, participants may probe a few webservers or not probe the network. In the attack stage, participants may attack any of the webservers or decide not to attack the webservers. Participants were randomly assigned to one of three between-subject conditions containing different honeypot proportions: small, medium, and large. With an increase in the proportion of honeypots, the honeypot and no-attack actions increased dramatically. We show how our findings are applicable in deception-based cyber scenarios.

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Optimal Placement of Honeypots for Network Defense

Cited by 13 publications

References 8 publications

Game Theoretic Honeypot Deployment in Smart Grid

Game Theoretic Honeypot Deployment in Smart Grid

Artificial Intelligence and Game Theory Models for Defending Critical Networks with Cyber Deception

Influence of different honeypot proportions on adversarial decisions in a deception game

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