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1. Invited Letter: Siemens Digital Industries Works with Education Partners to Offer Hands-On Learning

   Norris, Gail (April 2023, Journal of advanced technological education)

   Kazarinoff, Peter (Ed.)

   Siemens DI believes the future of manufacturing must be taught in our schools today. To facilitate this, we provide several educational programs that partner with technical colleges to provide hands-on learning and experience on our automation software and hardware. These programs include Siemens Cooperates with Education (SCE), Siemens Mechatronics Systems Certification Program (SMSCP), Lifelong Educational Advantage Program (LEAP), and Siemens Go-PLM. Participating as an industrial employer with the Preparing Technicians for the Future of Work ATE project team has provided Siemens with the ability to contribute to the tactile execution of educational priorities and influence the strategic direction of industry’s collaboration with educational institutions in a positive fashion. The ability to share concepts and practices with others in industry has contributed to changes in our approaches to educational partners and customer conversations. 

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2. Detecting Safety and Security Faults in PLC Systems with Data Provenance

   Al Farooq, Abdullah; Marquard, Jessica; George, Kripa; Moyer, Thomas (November 2019, IEEE International Symposium on Technologies for Homeland Security)

   Programmable Logic Controllers are an integral component for managing many different industrial processes (e.g., smart building management, power generation, water and wastewater management, and traffic control systems), and manufacturing and control industries (e.g., oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, automotive, and aerospace). Despite being used widely in many critical infrastructures, PLCs use protocols which make these control systems vulnerable to many common attacks, including man-in-the-middle attacks, denial of service attacks, and memory corruption attacks (e.g., array, stack, and heap overflows, integer overflows, and pointer corruption). In this paper, we propose PLC-PROV, a system for tracking the inputs and outputs of the control system to detect violations in the safety and security policies of the system. We consider a smart building as an example of a PLC-based system and show how PLC-PROV can be applied to ensure that the inputs and outputs are consistent with the intended safety and security policies. 

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3. Programmable Logic Controllers in the Context of Industry 4.0

   https://doi.org/10.1109/TII.2020.3007764  

   Sehr, Martin A.; Lohstroh, Marten; Weber, Mathew; Ugalde, Ines; Witte, Martin; Neidig, Joerg; Hoeme, Stephan; Niknami, Mehrdad; Lee, Edward A. (July 2020, IEEE Transactions on Industrial Informatics)

   Programmable Logic Controllers (PLCs) are an established platform, widely used throughout industrial automation but poorly understood among researchers. This paper gives an overview of the state of the practice, explaining why this settled technology persists throughout industry and presenting a critical analysis of the strengths and weaknesses of the dominant programming styles for today's PLC-based automation systems. We describe the software execution patterns that are standardized loosely in IEC 61131-3. We identify opportunities for improvements that would enable increasingly complex industrial automation applications while strengthening safety and reliability. Specifically, we propose deterministic, distributed programming models that embrace explicit timing, event-triggered computation, and improved security. 

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4. Evaluation of Neural Network Verification Methods for Air-to-Air Collision Avoidance

   https://doi.org/10.2514/1.D0255  

   Manzanas Lopez, Diego; Johnson, Taylor T.; Bak, Stanley; Tran, Hoang-Dung; Hobbs, Kerianne L. (January 2023, Journal of Air Transportation)

   Neural network approximations have become attractive to compress data for automation and autonomy algorithms for use on storage-limited and processing-limited aerospace hardware. However, unless these neural network approximations can be exhaustively verified to be safe, they cannot be certified for use on aircraft. An example of such systems is the unmanned Airborne Collision Avoidance System (ACAS) Xu, which is a very popular benchmark for open-loop neural network control system verification tools. This paper proposes a new closed-loop extension of this benchmark, which consists of a set of 10 closed-loop properties selected to evaluate the safety of an ownship aircraft in the presence of a co-altitude intruder aircraft. These closed-loop safety properties are used to evaluate five of the 45 neural networks that comprise the ACAS Xu benchmark (corresponding to co-altitude cases) as well as the switching logic between the five neural networks. The combination of nonlinear dynamics and switching between five neural networks is a challenging verification task accomplished with star-set reachability methods in two verification tools. The safety of the ownship aircraft under initial position uncertainty is guaranteed in every scenario proposed. 

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5. Ontology Modelling of Industrial Control System Ethical Hacking

   https://doi.org/10.34190/IWS.21.091  

   Heverin, T.; Chandnani, A.; Lopez, C.; Brahmhatt, N. (February 2021, International Conference on Cyber Warfare and Security)

   Industrial control systems (ICS) include systems that control industrial processes in critical infrastructure such as electric grids, nuclear power plants, manufacturing plans, water treatment systems, pharmaceutical plants, and building automation systems. ICS represent complex systems that contain an abundance of unique devices all of which may hold different types of software, including applications, firmware and operating systems. Due to their ability to control physical infrastructure, ICS have more and more become targets of cyber-attacks, increasing the risk of serious damage, negative financial impact, disruption to business operations, disruption to communities, and even the loss of life. Ethical hacking represents one way to test the security of ICS. Ethical hacking consists of using a cyber-attacker's perspective and a variety of cybersecurity tools to actively discover vulnerabilities and entry points for potential cyber-attacks. However, ICS ethical hacking represents a difficult task due to the wide variety of devices found on ICS networks. Most ethical hackers do not hold expertise or knowledge about ICS hardware, device computing elements, protocols, vulnerabilities found on these elements, and exploits used to exploit these vulnerabilities. Effective approaches are needed to reduce the complexity of ICS ethical hacking tasks. In this study, we use ontology modeling, a knowledge representation approach in artificial intelligence (AI), to model data that represent ethical hacking tasks of building automation systems. With ontology modeling, information is stored and represented in the form of semantic graphs that express individuals, their properties, and the relations between multiple individuals. Data are drawn from sources such as the National Vulnerability Database, ExploitDB, Common Weakness Enumeration (CWE), the Common Attack Pattern and Enumeration Classification (CAPEC), and others. We show, through semantic queries, how the ontology model can automatically link together entities such as software names and versions of ICS software, vulnerabilities found on those software instances, vulnerabilities found on the protocols used by the software, exploits found on those vulnerabilities, weaknesses that represent those vulnerabilities, and attacks that can exploit those weaknesses. The ontology modeling of ICS ethical hacking and the semantic queries run over the model can reduce the complexity of ICS hacking tasks. 

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