Special Session on Industry 4.0

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Cybersecurity for Manufacturers: Securing the Digitized and Connected Factory

As manufacturing becomes increasingly digitized and data-driven, manufacturers will find themselves at serious risk. Although there has yet to be a major successful cyberattack on a U.S. manufacturing operation, threats continue to rise. The complexities of multi-organizational dependencies and data-management in modern supply chains mean that vulnerabilities are multiplying. There is widespread agreement among manufacturers, government agencies, cybersecurity firms, and leading academic computer science departments that U.S. industrial firms are doing too little to address these looming challenges. Unfortunately, manufacturers in general do not see themselves to be at particular risk. This lack of recognition of the threat may represent the greatest risk of cybersecurity failure for manufacturers. Public and private stakeholders must act before a significant attack on U.S. manufacturers provides a wake-up call. Cybersecurity for the manufacturing supply chain is a particularly serious need. Manufacturing supply chains are connected, integrated, and interdependent; security of the entire supply chain depends on security at the local factory level. Increasing digitization in manufacturing— especially with the rise of Digital Manufacturing, Smart Manufacturing, the Smart Factory, and Industry 4.0, combined with broader market trends such as the Internet of Things (IoT)— exponentially increases connectedness. At the same time, the diversity of manufacturers—from large, sophisticated corporations to small job shops—creates weakest-link vulnerabilities that can be addressed most effectively by public-private partnerships. Experts consulted in the development of this report called for more holistic thinking in industrial cybersecurity: improvements to technologies, management practices, workforce training, and learning processes that span units and supply chains. Solving the emerging security challenges will require commitment to continuous improvement, as well as investments in research and development (R&D) and threat-awareness initiatives. This holistic thinking should be applied across interoperating units and supply chains.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/145442/1/MForesight_CybersecurityReport_Web.pd

Detection of cyber-attacks in systems with distributed control based on support vector regression

Concept of Industry 4.0 and implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in industrial plants are changing the way we manufacture. Introduction of industrial IoT leads to ubiquitous communication (usually wireless) between devices in industrial control systems, thus introducing numerous security concerns and opening up wide space for potential malicious threats and attacks. As a consequence of various cyber-attacks, fatal failures can occur on system parts or the system as a whole. Therefore, security mechanisms must be developed to provide sufficient resilience to cyber-attacks and keep the system safe and protected. In this paper we present a method for detection of attacks on sensor signals, based on e insensitive support vector regression (e-SVR). The method is implemented on publicly available data obtained from Secure Water Treatment (SWaT) testbed as well as on a real-world continuous time controlled electro-pneumatic positioning system. In both cases, the method successfully detected all considered attacks (without false positives)

Detection of cyber-attacks in systems with distributed control based on support vector regression

Concept of Industry 4.0 and implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in industrial plants are changing the way we manufacture. Introduction of industrial IoT leads to ubiquitous communication (usually wireless) between devices in industrial control systems, thus introducing numerous security concerns and opening up wide space for potential malicious threats and attacks. As a consequence of various cyber-attacks, fatal failures can occur on system parts or the system as a whole. Therefore, security mechanisms must be developed to provide sufficient resilience to cyber-attacks and keep the system safe and protected. In this paper we present a method for detection of attacks on sensor signals, based on e insensitive support vector regression (e-SVR). The method is implemented on publicly available data obtained from Secure Water Treatment (SWaT) testbed as well as on a real-world continuous time controlled electro-pneumatic positioning system. In both cases, the method successfully detected all considered attacks (without false positives)

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Think twice before you click! : exploring the role of human factors in cybersecurity and privacy within healthcare organizations

The urgent need to protect sensitive patient data and preserve the integrity of healthcare services has propelled the exploration of cybersecurity and privacy within healthcare organizations [1]. Recognizing that advanced technology and robust security measures alone are insufficient [2], our research focuses on the often-overlooked human element that significantly influences the efficacy of these safeguards. Our motivation stems from the realization that individual behaviors, decision-making processes, and organizational culture can be both the weakest link and the most potent tool in achieving a secure environment. Understanding these human dimensions is paramount as even the most sophisticated protocols can be undone by a single lapse in judgment. This research explores the impact of human behavior on cybersecurity and privacy within healthcare organizations and presents a new methodological approach for measuring and raising awareness among healthcare employees. Understanding the human influence in cybersecurity and privacy is critical for mitigating risks and strengthening overall security posture. Moreover, the thesis aims to place emphasis on the human aspects focusing more on the often-overlooked factors that can shape the effectiveness of cybersecurity and privacy measures within healthcare organizations. We have highlighted factors such as employee awareness, knowledge, and behavior that play a pivotal role in preventing security incidents and data breaches [1]. By focusing on how social engineering attacks exploit human vulnerabilities, we underline the necessity to address these human influenced aspects. The existing literature highlights the crucial role that human factors and awareness training play in strengthening cyber resilience, especially within the healthcare sector [1]. Developing well-customized training programs, along with fostering a robust organizational culture, is vital for encouraging a secure and protected digital healthcare setting [3]. Building on the recognized significance of human influence in cybersecurity within healthcare organizations, a systematic literature review became indispensable. The existing body of research might not have fully captured all ways in which human factors, such as psychology, behavior, and organizational culture, intertwined with technological aspects. A systematic literature review served as a robust foundation to collate, analyze, and synthesize existing knowledge, and to identify gaps where further research was needed. In complement to our systematic literature review and investigation of human factors, our research introduced a new methodological approach through a concept study based on an exploratory survey [4]. Recognizing the need to uncover intricate human behavior and psychology in the context of cybersecurity, we designed this survey to probe the multifaceted dimensions of cybersecurity awareness. The exploratory nature of the survey allowed us to explore cognitive, emotional, and behavioral aspects, capturing information that is often overlooked in conventional analyses. By employing this tailored survey, we were able to collect insights that provided a more textured understanding of how individuals within healthcare organizations perceive and engage with cybersecurity measures