Identifying Stealthy Attackers in a Game Theoretic Framework Using Deception

Basak, Anjon; Kamhoua, Charles A.; Sridhar, Varadharajan; Gutierrez, Marcus; Anwar, Ahmed H.; Kiekintveld, Christopher

doi:10.1007/978-3-030-32430-8_2

Cited by 10 publications

(11 citation statements)

References 7 publications

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“…They used signaling games with perfect Bayesian equilibrium to model the interactions between the defender and attacker. Basak et al [24] used cyberdeception tools to identify an attacker type as early as possible to take better defensive strategies. The attacker type is encapsulated in his actions and goals when planning an attack campaign.…”

Section: B Game-theoretic Defensive Deceptionmentioning

confidence: 99%

“…• Node or device compromise [2,3,12,13,14,15,23,24,30,58,63,65,73,81,87,99,100,104,106,111,135,141]: Some research does not specify the details of attack process. The authors only use "device compromising" to represent an attack.…”

Section: Attacks Countermeasured By Defensive Deception Techniquesmentioning

confidence: 99%

“…The authors only use "device compromising" to represent an attack. Some research discusses that an attacker can probe a target before attacking [2,3,12,13,14,15,63,65,81,87,99,100,111,135,141] while others only discuss the attacking actions [23,24,30,58,73,104,106].…”

Section: Attacks Countermeasured By Defensive Deception Techniquesmentioning

confidence: 99%

“…• Detection accuracy [1,21,24,23,27,32,45,60,88 [45] used an algorithm to discover fake Liker in social networks. The authors in [27,32,143] evaluated a masquerade attack detector based on AUC.…”

Section: A Metricsmentioning

confidence: 99%

“…• Mean time to detect attacks [24,23]: The effectiveness of a detection mechanism is also measured by how early their deception technique can capture the attacker.…”

Section: A Metricsmentioning

confidence: 99%

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Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey

Zhu¹,

Anwar²,

Wan³

et al. 2021

Preprint

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Defensive deception is a promising approach for cyberdefense. Although defensive deception is increasingly popular in the research community, there hasn't been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey paper focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.

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Section: B Game-theoretic Defensive Deceptionmentioning

confidence: 99%

Section: Attacks Countermeasured By Defensive Deception Techniquesmentioning

confidence: 99%

Section: Attacks Countermeasured By Defensive Deception Techniquesmentioning

confidence: 99%

Section: A Metricsmentioning

confidence: 99%

“…• Mean time to detect attacks [24,23]: The effectiveness of a detection mechanism is also measured by how early their deception technique can capture the attacker.…”

Section: A Metricsmentioning

confidence: 99%

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