HackIT: A Human-in-the-Loop Simulation Tool for Realistic Cyber Deception Experiments

Aggarwal, Palvi; Gautam, Aksh; Agarwal, Vaibhav; González, Cleotilde; Dutt, Varun

doi:10.1007/978-3-030-20488-4_11

Cited by 11 publications

(17 citation statements)

References 10 publications

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“…This shows that deception made them slower to move and attack. Aggarwal et al [4] developed a simulation tool, called HackIt, to study attacks in a network reconnaissance stage where participants studied the effect of introducing deception at different timing intervals. Their results showed that the attacker performed attacks on the honeypots more often than real machines.…”

Section: Empirical Game Experiments Using Human Subjectsmentioning

confidence: 99%

“…• Round trip-time [4,16,24,31,35,42,122]: The key role of DD is to delay the adversary processes [16]. A common example can be found that DD techniques can easily increase an attacker's reconnaissance or scanning time to identify vulnerable targets by increasing the attacker's confusion [122].…”

Section: A Metricsmentioning

confidence: 99%

“…The HackIt deception tool in [4] successfully increased the time taken by hackers to exploit a system. An attacker's deception detection time is of great significance to evaluate the effectiveness of cyberdeception as in [24].…”

Section: A Metricsmentioning

confidence: 99%

“…3) Emulation Testbed-Based Evaluation [42,56,137]: Emulation network environments have been proposed to dynamically place deception resources [137], deploy code obfuscation techniques on real-like decompilers [42], and to deploy DD techniques in emulated SDN-based data center network testbed. [2,3,4,16,17,60,84,88,89,144]: An application for data collection has been developed [2,3,4]. Experiments with human-in-the-loop with human participants (e.g.…”

Section: B Evaluation Testbedsmentioning

confidence: 99%

See 3 more Smart Citations

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: Empirical Game Experiments Using Human Subjectsmentioning

confidence: 99%

Section: A Metricsmentioning

confidence: 99%

Section: A Metricsmentioning

confidence: 99%

Section: B Evaluation Testbedsmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhu¹,

Anwar²,

Wan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Some researchers have proposed games to study the role of deception in cybersecurity mathematically ( Garg and Grosu, 2007 ; Kiekintveld et al, 2015 ; Aggarwal et al, 2017 ). However, more recently, researchers have investigated human decisions in the presence of deception in abstract Stackelberg security games ( Cranford et al, 2018 ) as well as applied games like HackIT ( Aggarwal et al, 2019 , 2020 ). Here, researchers have relied upon behavioral game theory ( Camerer, 2003 ) and cognitive theories like instance-based learning theory (IBLT) ( Gonzalez et al, 2003 ; Gonzalez and Dutt, 2011 , 2012 ; Dutt and Gonzalez, 2012 ; Dutt et al, 2013 ) to understand human decisions in different cyberattack scenarios ( Aggarwal et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Influence of Network Size on Adversarial Decisions in a Deception Game Involving Honeypots

et al. 2020

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Deception via honeypots, computers that pretend to be real, may provide effective ways of countering cyberattacks in computer networks. Although prior research has investigated the effectiveness of timing and amount of deception via deceptionbased games, it is unclear as to how the size of the network (i.e., the number of computer systems in the network) influences adversarial decisions. In this research, using a deception game (DG), we evaluate the influence of network size on adversary's cyberattack decisions. The DG has two sequential stages, probe and attack, and it is defined as DG (n, k, γ), where n is the number of servers, k is the number of honeypots, and γ is the number of probes that the adversary makes before attacking the network. In the probe stage, participants may probe a few web servers or may not probe the network. In the attack stage, participants may attack any one of the web servers or decide not to attack the network. In a laboratory experiment, participants were randomly assigned to a repeated DG across three different between-subject conditions: small (20 participants), medium (20 participants), and large (20 participants). The small, medium, and large conditions used DG (2, 1, 1), DG (6, 3, 3), and DG (12, 6, 6) games, respectively (thus, the proportion of honeypots was kept constant at 50% in all three conditions). Results revealed that in the small network, the proportions of honeypot and no-attack actions were 0.20 and 0.52, whereas in the medium (large) network, the proportions of honeypot and no-attack actions were 0.50 (0.50) and 0.06 (0.03), respectively. There was also an effect of probing actions on attack actions across all three network sizes. We highlight the implications of our results for networks of different sizes involving deception via honeypots.

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Diversifying Deception: Game-Theoretic Models for Two-Sided Deception and Initial Human Studies

Miah

Aggarwal²,

Gutierrez³

et al. 2022

Advances in Information Security

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HackIT: A Human-in-the-Loop Simulation Tool for Realistic Cyber Deception Experiments

Cited by 11 publications

References 10 publications

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

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

Influence of Network Size on Adversarial Decisions in a Deception Game Involving Honeypots

Diversifying Deception: Game-Theoretic Models for Two-Sided Deception and Initial Human Studies

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