| dc.description.abstract | The convergence of 5G technology and smart factory has marked a new era of industrial transformation. Smart factories leverage the power of 5G networks and cutting-edge technologies like the Industrial Internet of Things (IIoT) and artificial intelligence (AI), and edge computing to optimize production processes, enhance efficiency, and drive innovation. However, as smart factories become increasingly connected and digitized, they also become more vulnerable to cybersecurity threats and disruptions. Therefore, ensuring the security and resilience of 5G-enabled smart factories is paramount to maintaining operational continuity, protecting valuable assets, and safeguarding against potential risks.
Wireless communications, such as 4G and 5G, play a crucial role in our daily lives, making security a critical concern. These technologies have evolved significantly to meet our growing demands, incorporating new features and advancements. However, this evolution also introduces new security threats and vulnerabilities. This dissertation examines the security threats and weaknesses present in 5G-IIoT smart factories, which continue to evolve with both legacy and modern wireless communication software components and paradigms.
Wireless communications, including 4G and 5G, are integral to our daily lives, making security a critical concern. While these technologies have evolved significantly to meet growing demands and incorporate new features, they also introduce new security threats and vulnerabilities. This dissertation examines the evolving security threats and weaknesses in 5G-IIoT smart factories, focusing on both legacy and modern wireless communication software components and paradigms.This dissertation takes an initial step toward addressing industrial needs by presenting a general method for identifying novel security issues in 5G smart factories, specifically at the intersection of IIoT and 5G communication. This contribution is based on an analysis of IIoT and smart factory security incidents, as well as emerging trends in 5G technologies and attacker capabilities.
Establishes a wireless, non-intrusive smart sensor system and a 5G edge computing gateway, alongside deploying active detection and protection systems for the cybersecurity of 5G smart factories. These systems meet the needs of monitoring equipment power and vibration, ensuring electrical safety, predictive maintenance, and cybersecurity protection. By using non-intrusive smart sensor systems and 5G edge computing gateways to identify mechanical faults and assess equipment health, predictive maintenance and upkeep can be performed, reducing production disruptions and decreasing the time and human resources required for future maintenance and monitoring.
The empirical approach to active detection and protection systems in smart factories employs machine learning to establish behavioral patterns in smart factory networks, featuring automatic deployment and threat detection capabilities. Furthermore, the dissertation analyzes attacks and threats to IIoT and 5G networks, proposing corresponding mitigation measures for 5G smart factories.The dissertation′s methodology involves an in-depth analysis of the security and resilience properties of 5G-enabled smart factories. By conducting real-world experiments, we identify previously unknown vulnerabilities impacting 5G security.
The methodology follows the ABCD approach, which stands for Active Scanner (active vulnerability information detection), Behavior Mesh Monitor (network behavior monitoring), Correlation in Defense (correlated defense of heterogeneous networks), and Dynamic Honeypot (construction of virtual honeypot environments). This framework establishes a secure 5G smart factory field trial. Data sources come from raw data generated by the actual operation of 5G smart factories, which are analyzed and verified. The research results show that in 5G smart factories, by analyzing data captured by smart sensors to identify mechanical faults and assess equipment health, fault pattern diagnosis accuracy can reach 90% through the analysis of time-domain signal features. Additionally, the active detection and protection system can detect network anomalies and abnormal equipment behavior, achieving a detection rate of over 90% and an equipment identification rate of 93%.
This dissertation involved constructing attacks to exploit vulnerabilities, demonstrating the practicality of our findings. Various security issues were uncovered in each system analyzed, and several defense and mitigation solutions were presented to address these issues. The summary of this resaerch results is as follows: First, several security issues in IIoT networks that can be exploited by network adversaries were identified. Second, security weaknesses in signaling protocols of mobile communication systems were uncovered. Additionally, a MITRE ATT&CK framework was presented to model the threats and attacks on 5G mobile networks.Throughout this dissertation, numerous technical vulnerabilities affecting 5G-enabled smart factory were uncovered and addressed. Crucially, a versatile approach for conducting impactful security research where 5G Stand Alone (SA) networks intersect with the Industrial Internet of Things (IIoT) in smart factory was introduced and verified. The primary objective are to develop a Proactive Detection and Defense platform and validate its performance and security features in a reproducible test environment. | en_US |