Industry 5.0 is transforming the industrial paradigm by placing humans at the center of manufacturing and service processes. This approach, which emphasizes the synergistic collaboration between humans and cyber-physical systems (CPS), goes beyond the purely productivity-oriented goals of Industry 4.0 and prioritizes sustainability, resilience, and human well-being. However, the evolving nature of human-machine interactions poses complex security challenges. Traditional information security management systems (ISMS), which are based on the PDCA cycle and top-down approaches, face limitations in adapting to operational dynamics and receiving qualitative feedback from frontline workers. This paper introduces a complementary human-centric framework, “Low-Level ISMS” (LL-ISMS), while identifying emerging security risks in these complex ecosystems. The framework operates as a bottom-up feedback loop and focuses on four key stages: purposeful preparation (Do’), practical perception (Feel), critical analysis (Think), and active participation (Help). By connecting operational insights from employees to management decisions, LL-ISMS significantly increases the effectiveness of security management in Industry 5.0 environments and fosters a shared and dynamic security culture.
INTRODUCTION: Cyber incidents are increasingly shaped not only by technical severity but also by how risk signals are amplified through AI-mediated information ecosystems. Deepfakes, synthetic media, algorithmic amplification, and AI-generated misinformation can intensify public fear, distort trust, and trigger disproportionate societal responses. OBJECTIVES: This paper develops the Resonant Risk Model as a sociotechnical extension of the Social Amplification of Risk Framework for AI-era cybersecurity. It also proposes the Resonant Risk Management Framework to support perception-aware cyber risk governance. METHODS: The study uses theory-building, interdisciplinary literature synthesis, structured case selection, and comparative case analysis. Six cases are assessed using dimensions including technical severity, amplification channels, resonance factors, public perception, societal ripple effects, and feedback loops. RESULTS: The analysis shows that high technical severity does not always produce high public resonance. SolarWinds showed very high technical severity but moderate public resonance, while AI-driven misinformation and deepfake cases produced high trust erosion despite lower direct technical impact. CONCLUSION: The paper argues that cyber resilience must include perception monitoring, rapid communication, misinformation correction, and trust recovery alongside technical controls.
The increasing demand for privacy has driven the adoption of privacy-enhancing tools such as VPNs, but website fingerprinting – the analysis of packet metadata like packet size and number of packets – still poses a substantial risk. Website fingerprinting allows adversaries to predict a victim’s web usage based on their browsing patterns, effectively creating a “fingerprint”. Recent studies have largely focused on laboratory settings and have assumed a simplified model: a victim visits a single website at a time and that all network packets can be observed. However, a new private browser extension, WebTracker, deployed with real users, shows that observed browsing patterns are significantly different from those previously assumed. Users’ behavior frequently exhibits defensive strategies, such as multiple websites overlapping and downloading simultaneously, which can interfere with website fingerprinting. A study of international users demonstrated that over 15% of websites overlap with at least another, with an average overlap time of 66 seconds, while a US-based study showed only 0.72% of websites overlap. Moreover, these overlaps typically occur shortly after the initial website download. These findings suggest that the beginning of a website is more crucial than the end for website fingerprinting attacks, highlighting the need for more analysis of webbrowsing behavior.
The growing reliance on Location-Based Services (LBS) has intensified privacy risks, as the continuous collection of sensitive user location data exposes individuals to potential re-identification and unauthorised tracking. This paper presents a hybrid privacy-preserving framework that combines the Diameter-Bounded DBSCAN clustering algorithm for spatial k-anonymity with an adaptive Laplace mechanism for ε-differential privacy. This integration ensures the formation of compact anonymity groups while maintaining high data utility. Experimental evaluation on the real-world GeoLife dataset demonstrates 85.1% query accuracy, 0.14 trajectory distortion (EDR), and average query latency below 100 milliseconds for 20,000 users, outperforming DPPS and AdaptiveGrid baselines. Comprehensive sensitivity analysis of the diameter threshold (dmax) and evaluation of suppression bias confirm the framework’s robustness, scalability, and practical suitability for real-time LBS deployment.
INTRODUCTION: The current volume and sophistication of cyber threats are beyond overshadowing the security capabilities of traditional reactive security approaches. Herein, we present a new cybersecurity framework that incorporates real-time threat intelligence with adaptive deception technologies for the proactive defense of digital infrastructures. OBJECTIVES: The objectives of this research include: (1) develop an AI-driven cybersecurity framework, (2) incorporate real-time threat intelligence and deception-based active defense approaches, and (3) assess performance in simulated and real-world cyber-attack scenarios. METHODS: The proposed cyber-defense framework uses machine learning approaches, automated deception technologies (e.g., honeypots, moving target defense), and real-time threat intelligence feeds. The framework is constructed in a modular architecture and tested in simulation environments with real-time attack emulation. RESULTS: The framework performed with over 93% of threats visible, an adaptive response time < 2 seconds, and < 12% overhead imposed on the system. The framework achieved > 85% threat prevention, measured long recovery time, and measured system integrity improvements. CONCLUSION: The conclusion of this work illustrates that a proactive cybersecurity framework can be achieved through the integration of AI-enabled adaptive response with real-time threat intelligence. This work represents an advancement toward intelligent, self-learning systems capable of anticipating and responding to developing cyber threats with minimal human intervention.
