A data-driven security quantification framework for IoT-based systems

• URL: http://arxiv.org/abs/2606.16561v1
• Date: Mon, 15 Jun 2026 11:04:04 GMT
• Title: A data-driven security quantification framework for IoT-based systems
• Abstract summary: This study introduces a data-driven probabilistic security framework for IoT-based safety-critical systems.<n>It integrates Model-Based Systems Engineering (SEMB), Attack Tree Analysis (ATA) and empirical vulnerability data.<n>It quantifies system compromise probabilities, identifies likely causes of attacks, and prioritises mitigation strategies.
• Score: 1.7499351967216337
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Internet of Things (IoT) is integral to modern cyber-physical systems. Quantitative cybersecurity assessment in IoT environments remains challenging due to heterogeneous system architectures, evolving threat landscapes, and the limited availability of reliable probabilistic exploitability data. Although Attack Tree Analysis (ATA) provides a structured framework for modelling potential attack paths leading to system compromise, conventional ATA quantification often relies on subjective expert judgement or heuristic scoring schemes, which can introduce uncertainty and reduce analytical reproducibility. This study introduces a data-driven probabilistic security framework for IoT-based safety-critical systems by integrating Model-Based Systems Engineering (MBSE), ATA, and empirical vulnerability data. In the proposed framework, SysML models capture system architecture, from which attack trees are derived. Vulnerabilities are mapped as Basic Attack Steps and assigned exploitation probabilities using the Exploit Prediction Scoring System (EPSS). The attack tree is then represented as a Bayesian Network, enabling probabilistic reasoning, diagnostic inference, and vulnerability criticality analysis. The framework quantifies system compromise probabilities, identifies likely causes of attacks, and prioritises mitigation strategies. By combining architecture-driven modelling with real-world vulnerability intelligence, it provides a rigorous, reproducible approach for cybersecurity risk assessment in complex IoT environments.

Related papers

• SoK: How Frontier AI Reshapes System-Level Security Risk Dynamics in Critical Infrastructure [8.022874342690155]
  This paper introduces a five-dimensional risk-dynamics framework that characterizes how FAIs CI security across the lifecycle.<n>Rather than enumerating threats, the framework identifies recurring mechanisms that jointly determine system-level risk.
  arXiv  Detail & Related papers  (2026-08-03T01:30:35Z)
• Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework [0.8496907566303933]
  This study is based on the CICIDS 2017 dataset for the development and testing of intrusion detection system models and cyber risk prediction models based on machine learning.<n>The proposed framework presents the reliability and resilience of the model by having various performance measures such as accuracy, precision, recall, F1 score, ROC-AUC, and false positive rate.
  arXiv  Detail & Related papers  (2026-06-04T05:05:14Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• An Approach to Generate Attack Graphs with a Case Study on Siemens PCS7 Blueprint for Water Treatment Plants [0.6935490869740528]
  This paper introduces a semi-automated approach for generating attack graphs in ICS environments to visualize and analyze multi-step attack scenarios.<n>We show how a single point of failure can compromise network segmentation and how patching a critical vulnerability can protect an entire security zone.
  arXiv  Detail & Related papers  (2026-03-26T00:06:20Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Bayesian and Multi-Objective Decision Support for Real-Time Cyber-Physical Incident Mitigation [6.852472228194646]
  This research proposes a real-time, adaptive decision-support framework for mitigating cyber incidents in cyber-physical systems.<n>It is developed in response to an increasing reliance on these systems within critical infrastructure and evolving adversarial tactics.
  arXiv  Detail & Related papers  (2025-08-31T09:47:38Z)
• A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents [45.53643260046778]
  Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents.<n>These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities.
  arXiv  Detail & Related papers  (2025-06-30T13:34:34Z)
• Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation [42.350737545269105]
  complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors.<n>We propose to meet those risks with a declarative approach to describe cyber power systems and automatically evaluate security and safety controls.
  arXiv  Detail & Related papers  (2025-06-14T12:07:44Z)
• Modeling Interdependent Cybersecurity Threats Using Bayesian Networks: A Case Study on In-Vehicle Infotainment Systems [0.0]
  This paper reviews the application of Bayesian Networks (BNs) in cybersecurity risk modeling.<n>A case study is presented in which a STRIDE-based attack tree for an automotive In-Vehicle Infotainment (IVI) system is transformed into a BN.
  arXiv  Detail & Related papers  (2025-05-14T01:04:45Z)
• Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems [44.61435605872856]
  We introduce a practical robustness definition grounded in distributional robustness, explicitly tailored to industrial CPS.<n>Our framework simulates realistic disturbances, such as sensor drift, noise and irregular sampling, enabling thorough robustness analyses of forecasting models.
  arXiv  Detail & Related papers  (2025-04-04T14:50:48Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
  Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks. Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete. This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
  arXiv  Detail & Related papers  (2023-12-27T10:44:58Z)
• Automated Security Assessment for the Internet of Things [6.690766107366799]
  We propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions. This security model automatically assesses the security of the IoT network by capturing potential attack paths.
  arXiv  Detail & Related papers  (2021-09-09T04:42:24Z)

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.