Conducting IoT Vulnerability Risk Assessments in Smart Factory Networks: Tools and Techniques
Keywords:
Smart Factory, IoT Cybersecurity, Vulnerability Assessment, Industry 4.0, AI-Driven Analytics, Regulatory ComplianceAbstract
In 2024, smart factories, powered by Internet of Things (IoT) devices, drive Industry 4.0, enhancing automation and efficiency but introducing significant cybersecurity risks. With 70% of smart factory networks vulnerable to IoT-related attacks, effective vulnerability risk assessments are critical to safeguard operations, data, and supply chains. This paper proposes a comprehensive framework for conducting IoT vulnerability risk assessments in smart factory networks, integrating advanced tools (e.g., Nessus, OpenVAS, Wireshark) and techniques (e.g., penetration testing, threat modeling, AI-driven analytics). Employing a mixed-method approach, the study combines a systematic literature review of 180 peer-reviewed articles and industry reports (2018–2024), tool development, and pilot testing across 10 smart factories in automotive, electronics, and pharmaceutical sectors in North America, Europe, and Asia. The proposed framework achieves 95% vulnerability detection accuracy, reduces risk exposure by 40%, and cuts assessment time by 30% compared to traditional methods. Key findings highlight the framework’s scalability across 100–10,000 IoT devices, compatibility with legacy systems, and compliance with standards like ISO 27001 and NIST 800-53. Challenges include high initial costs ($10,000–$50,000), technical complexity, and regulatory fragmentation, while opportunities involve AI-enhanced threat prediction, blockchain for auditability, and zero-trust integration. The study contributes to cybersecurity and smart manufacturing literature by offering a practical, scalable framework bridging technical, operational, and regulatory needs. For smart factory operators, it provides tools to mitigate risks, ensure compliance, and enhance resilience. Policymakers gain insights to standardize regulations, while researchers benefit from a foundation for exploring AI-driven assessments and SME-focused solutions. Future directions include quantum-resistant cryptography, automated remediation, and ethical frameworks for IoT security. By addressing these issues, this paper underscores the transformative potential of IoT vulnerability risk assessments in securing smart factory networks, fostering resilient, secure, and efficient manufacturing ecosystems.
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