A Descriptive Model for Modelling Attacker Decision-Making in Cyber-Deception

• URL: http://arxiv.org/abs/2512.03641v1
• Date: Wed, 03 Dec 2025 10:23:33 GMT
• Title: A Descriptive Model for Modelling Attacker Decision-Making in Cyber-Deception
• Authors: B. R. Turner, O. Guidetti, N. M. Karie, R. Ryan, Y. Yan,
• Abstract summary: This paper presents a descriptive model that incorporates the psychological and strategic elements shaping this decision.<n>The framework provides a structured method for analysing engagement decisions in cyber-deception scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Cyber-deception is an increasingly important defensive strategy, shaping adversarial decision making through controlled misinformation, uncertainty, and misdirection. Although game-theoretic, Bayesian, Markov decision process, and reinforcement learning models offer insight into deceptive interactions, they typically assume an attacker has already chosen to engage. Such approaches overlook cognitive and perceptual factors that influence an attacker's initial decision to engage or withdraw. This paper presents a descriptive model that incorporates the psychological and strategic elements shaping this decision. The model defines five components, belief (B), scepticism (S), deception fidelity (D), reconnaissance (R), and experience (E), which interact to capture how adversaries interpret deceptive cues and assess whether continued engagement is worthwhile. The framework provides a structured method for analysing engagement decisions in cyber-deception scenarios. A series of experiments has been designed to evaluate this model through Capture the Flag activities incorporating varying levels of deception, supported by behavioural and biometric observations. These experiments have not yet been conducted, and no experimental findings are presented in this paper. These experiments will combine behavioural observations with biometric indicators to produce a multidimensional view of adversarial responses. Findings will improve understanding of the factors influencing engagement decisions and refine the model's relevance to real-world cyber-deception settings. By addressing the gap in existing models that presume engagement, this work supports more cognitively realistic and strategically effective cyber-deception practices.

Related papers

• Behavioral Outcomes of Human Cognitive Security within an Integrative Modeling Framework [0.0]
  Information-based threats pose challenges to human cognitive processes and behavior.<n>There is no well-defined construct for characterizing the degree to which information-based threats influence changes in human judgments and decision-making.<n>Here, we introduce a human cognitive security construct focused on linking information-based threats to observable outcomes.
  arXiv  Detail & Related papers  (2026-03-02T01:26:12Z)
• Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies [0.7036032466145113]
  This research explores the ability to model and detect when hackers exhibit ambiguity aversion.<n>By operationalizing this cognitive trait, our work provides a foundational component for developing adaptive cognitive defense strategies.
  arXiv  Detail & Related papers  (2025-12-08T23:26:08Z)
• LLMs as Strategic Agents: Beliefs, Best Response Behavior, and Emergent Heuristics [0.0]
  Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior.<n>We show that current frontier models exhibit belief-coherent best-response behavior at targeted reasoning memorization.<n>Under increasing complexity, explicit recursion gives way to internally generated rules of choice that are stable, model-specific, and distinct from known human biases.
  arXiv  Detail & Related papers  (2025-10-12T21:40:29Z)
• Perception Graph for Cognitive Attack Reasoning in Augmented Reality [12.005631730339708]
  We introduce a novel model designed to reason about human perception within augmented reality systems.<n>Our model operates by first mimicking the human process of interpreting key information from an MR environment.<n>We demonstrate how the model can compute a quantitative score that reflects the level of perception distortion.
  arXiv  Detail & Related papers  (2025-08-30T20:48:32Z)
• A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment [0.33928435949901725]
  This paper introduces a framework to analyze and secure decision-support systems trained with Deep Reinforcement Learning (DRL)<n>We validate our framework, visualize agent behavior, and evaluate adversarial outcomes within the context of a custom-built strategic game, CyberStrike.
  arXiv  Detail & Related papers  (2025-05-27T16:41:23Z)
• Investigating and unmasking feature-level vulnerabilities of CNNs to adversarial perturbations [3.4530027457862]
  This study explores the impact of adversarial perturbations on Convolutional Neural Networks (CNNs) We introduce the Adversarial Intervention framework to study the vulnerability of a CNN to adversarial perturbations.
  arXiv  Detail & Related papers  (2024-05-31T08:14:44Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [72.80902932543474]
  Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker's policy is challenging. We propose a data-driven representation of decision-making behavior that inheres transparency by design, accommodates partial observability, and operates completely offline.
  arXiv  Detail & Related papers  (2023-10-28T13:06:14Z)
• Interpretable Imitation Learning with Dynamic Causal Relations [65.18456572421702]
  We propose to expose captured knowledge in the form of a directed acyclic causal graph. We also design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-30T20:59:42Z)
• Neural Causal Models for Counterfactual Identification and Estimation [62.30444687707919]
  We study the evaluation of counterfactual statements through neural models. First, we show that neural causal models (NCMs) are expressive enough. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions.
  arXiv  Detail & Related papers  (2022-09-30T18:29:09Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Representations of epistemic uncertainty and awareness in data-driven strategies [0.0]
  We present a theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We look at inequivalent knowledge representations in terms of inferences, preference relations, and information measures. We discuss some implications of the proposed model for data-driven strategies.
  arXiv  Detail & Related papers  (2021-10-21T21:18:21Z)
• Inverse Active Sensing: Modeling and Understanding Timely Decision-Making [111.07204912245841]
  We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure. We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
  arXiv  Detail & Related papers  (2020-06-25T02:30:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.