BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents

• URL: http://arxiv.org/abs/2602.19410v1
• Date: Mon, 23 Feb 2026 01:06:16 GMT
• Title: BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents
• Authors: Duy Anh Ta, Farnaz Farid, Farhad Ahamed, Ala Al-Areqi, Robert Beutel, Tamara Watson, Alana Maurushat,
• Abstract summary: We propose a conceptual security framework that integrates a hybrid CNN-LSTM model to analyze biometric and environmental data for context-aware security decisions.<n>The model achieves 84% accuracy, demonstrating its ability to reliably detect conditions that lead to elevated human-centred cyber risk.
• Score: 0.3015442485490763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern organizations increasingly face cybersecurity incidents driven by human behaviour rather than technical failures. To address this, we propose a conceptual security framework that integrates a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to analyze biometric and environmental data for context-aware security decisions. The CNN extracts spatial patterns from sensor data, while the LSTM captures temporal dynamics associated with human error susceptibility. The model achieves 84% accuracy, demonstrating its ability to reliably detect conditions that lead to elevated human-centred cyber risk. By enabling continuous monitoring and adaptive safeguards, the framework supports proactive interventions that reduce the likelihood of human-driven cyber incidents

Related papers

• Securing AI Agents in Cyber-Physical Systems: A Survey of Environmental Interactions, Deepfake Threats, and Defenses [2.6726842616701703]
  This survey provides a comprehensive review of security threats targeting AI agents in cyber-physical systems.<n>We focus on environmental interactions, deepfake-driven attacks, and MCP-mediated vulnerabilities.<n>We quantitatively illustrate how timing, noise, and false-positive costs constrainable defenses.
  arXiv  Detail & Related papers  (2026-01-28T02:33:24Z)
• AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers [69.56534335936534]
  AmbShield is an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers'<n>In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices.
  arXiv  Detail & Related papers  (2026-01-14T20:56:50Z)
• Multi-Agent-Driven Cognitive Secure Communications in Satellite-Terrestrial Networks [58.70163955407538]
  Malicious eavesdroppers pose a serious threat to private information via satellite-terrestrial networks (STNs)<n>We propose a cognitive secure communication framework driven by multiple agents that coordinates spectrum scheduling and protection through real-time sensing.<n>We exploit generative adversarial networks to produce adversarial matrices, and employ learning-aided power control to set real and adversarial signal powers for protection layer.
  arXiv  Detail & Related papers  (2026-01-06T10:30:41Z)
• Threat Detection in Social Media Networks Using Machine Learning Based Network Analysis [0.0]
  This paper introduces a threat detection framework based on machine learning that can be used to classify malicious behavior in the social media network environment.<n>A model of artificial neural network (ANN) is then created to acquire intricate, non-linear tendencies of malicious actions.<n>The proposed model is tested on conventional performance metrics, such as accuracy, accuracy, recall, F1-score, and ROC-AUC, and shows good detection and high levels of strength.
  arXiv  Detail & Related papers  (2026-01-05T22:14:41Z)
• OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
  Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
  arXiv  Detail & Related papers  (2025-10-28T13:22:39Z)
• End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems [5.804791448287085]
  This chapter discusses an integrated co-simulation testbed that links CARLA for 3D environment and sensor modeling, SUMO for microscopic traffic simulation and control, and OMNeT++ for V2X communication simulation.<n>The co-simulation testbed enables end-to-end experimentation, vulnerability identification, and mitigation benchmarking.<n>To illustrate its capabilities, the chapter incorporates a case study on a C-V2X proactive safety alert system enhanced with post-quantum cryptography.
  arXiv  Detail & Related papers  (2025-09-20T01:21:54Z)
• The Cybersecurity of a Humanoid Robot [0.5958112901546286]
  This report presents a comprehensive security assessment of a production humanoid robot platform.<n>We uncovered a complex security landscape characterized by both sophisticated defensive mechanisms and critical vulnerabilities.<n>This work contributes empirical evidence for developing robust security standards as humanoid robots transition from research curiosities to operational systems in critical domains.
  arXiv  Detail & Related papers  (2025-09-17T15:37:09Z)
• Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence [0.0]
  This manuscript explores how the convergence of agentic AI and Frontier AI is transforming cybersecurity.<n>We examine the roles of real time monitoring, automated incident response, and perpetual learning in forging a resilient, dynamic defense ecosystem.<n>Our vision is to harmonize technological innovation with unwavering ethical oversight, ensuring that future AI driven security solutions uphold core human values of fairness, transparency, and accountability while effectively countering emerging cyber threats.
  arXiv  Detail & Related papers  (2025-02-28T20:23:35Z)
• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  This paper introduces a novel framework for detecting instability in smart grids using only stable data.<n>It achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks.<n>Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

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.