The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

Recently, the use of IoT devices and sensors has been rapidly increased which also caused data generation (information and logs), bandwidth usage, and related phenomena to be increased. To our best knowledge, a standard definition for the integration of fog computing with IoT is emerging now. This integration will bring many opportunities for the researchers, especially while building cyber-security related solutions. In this study, we surveyed about the integration of fog computing with IoT and its implications. Our goal was to find out and emphasize problems, specifically security related problems that arise with the employment of fog computing by IoT. According to our findings, although this integration seems to be non-trivial and complicated, it has more benefits than the implications.

Today's online marketing industry has widely employed email tracking techniques, such as embedding a tiny tracking pixel, to track email opens of potential customers and measure marketing effectiveness. However, email tracking could allow miscreants to collect metadata information associated with email reading without user awareness and then leverage the information for stealthy surveillance, which has raised serious privacy concerns. In this paper, we present an in-depth and comprehensive study on the privacy implications of email tracking. First, we develop an email tracking system and perform realworld tracking on hundreds of solicited crowdsourcing participants. We estimate the amount of privacy-sensitive information available from email reading, assess privacy risks of information leakage, and demonstrate how easy it is to launch a long-term targeted surveillance attack in real scenarios by simply sending an email with tracking capability. Second, we investigate the prevalence of email tracking through a large-scale measurement, which includes more than 44,000 email samples obtained over a period of seven years. Third, we conduct a user study to understand users' perception of privacy infringement caused by email tracking. Finally, we evaluate existing countermeasures against email tracking and propose guidelines for developing more comprehensive and fine-grained prevention solutions.

Industry 4.0 and 5.0 offer a promising framework for connecting electro-mechanical systems to cyberspace, enabling real-time access, telecontrol, human-machine collaboration, and intelligent automation of industrial operations. While horizontal and vertical interoperability serve as critical enablers of this ecosystem, heterogeneity among entities and the lack of standardized governance in interoperability allow cybercriminals to exploit structural vulnerabilities. These weaknesses and unknown bugs provide avenues for cyber-attackers to breach systems, conduct espionage, sabotage assets, and extort organizations, threatening IT and OT infrastructures, finances, reputations, and even human lives. This survey paper discusses cybersecurity and privacy threats within the Industry 4.0 and 5.0 ecosystems, their potential impact on industrial processes and peripherals, and the security challenges associated with the transition from Industry 4.0 to 5.0. To identify research gaps and vulnerabilities, we examine the architecture and components of diverse industrial frameworks and establish functional mappings using IIRA and RAMI models. Following a comprehensive threat modeling approach, we present a layered taxonomy of cyber-threats, classified based on their nature, behavior, and execution characteristics. To assist network administrators and security professionals, we propose a threat prioritization framework based on likelihood, detectability, impact severity, and operational consequences. Furthermore, we outline perspective-based cybersecurity challenges that expose deficiencies in current protective measures. As countermeasures, we advocate for AI-driven, blockchain-enabled, edge-computing-based, and privacy-preserving security solutions to defend against threats and mitigate potential damages. We also elaborate on key standardization initiatives, nation-specific privacy regulations, and ongoing research efforts focused on safeguarding the security and privacy of Industry 4.0 and beyond. The paper concludes by summarizing key lessons learned, identifying unresolved research questions, and suggesting future directions for a secure and resilient Industry 5.0 paradigm.

This study investigates product abuse, reconciliation challenges, and cybersecurity risks in warehouse management systems (WMS) within increasingly digitized supply chain environments. As warehouses evolve into data-driven operational hubs, vulnerabilities such as data manipulation, insider threats, and fraudulent activities pose significant risks to financial accountability and system integrity. To address these challenges, this research proposes a security-centric WMS framework that integrates blockchain-based immutable logging, Internet of Things (IoT)-enabled tracking, and artificial intelligence (AI)-driven anomaly detection. The methodology follows a hybrid iterative–incremental development approach, supported by real-world deployment of a prototype WMS implemented using a scalable microservices architecture. Over a five-year operational period, the system processed more than 10 million transactions with no recorded successful cybersecurity incidents leading to data breaches, operational compromise, or unauthorized system access, while achieving improvements in reconciliation accuracy, operational efficiency, and fraud detection capabilities. Results demonstrate reductions in manual reconciliation efforts, mispricing incidents, and operational losses, while maintaining high system availability and low latency. In addition, the reported 18–22% improvement associated with AI-assisted anomaly detection is presented as a simulation-based projection rather than a production-validated measurement. The findings indicate that combining secure software engineering practices with automation, auditability, and advanced analytics can significantly enhance transparency and resilience in warehouse operations. The study concludes that integrating decentralized and intelligent technologies provides a viable pathway toward secure, privacy-preserving, and abuse-resistant warehouse ecosystems.