Endpoint devices are integral in the realisation of any industrial cyber-physical system (ICPS) application. As part of the work of promoting safer and more secure industrial Internet of Things (IIoT) networks and devices, the Industrial Internet Consortium (IIC) and the OpenFog Consortium have developed security framework specifications detailing security techniques and technologies that should be employed during the design of an IIoT network. Previous work in establishing cryptographic services on platforms intended for wireless sensor networks (WSN) and the Internet of Things (IoT) has concluded that security mechanisms cannot be implemented using software libraries owing to the lack of memory and processing resources, the longevity requirements of the processor platforms, and the hard real-time requirements of industrial operations. Over a decade has passed since this body of knowledge was created, however, and IoT processors have seen a vast improvement in the available operating and memory resources while maintaining minimal power consumption. This study aims to update the body of knowledge regarding the provision of security services on an IoT platform by conducting a detailed analysis regarding the performance of new generation IoT platforms when running software cryptographic services. The research considers execution time, power consumption and memory occupation and works towards a general, implementable design of a secure, IIoT edge device. This is realised by identifying security features recommended for IIoT endpoint devices; identifying currently available security standards and technologies for the IIoT; and highlighting the trade-offs that the application of security will have on device size, performance, memory requirements and monetary cost. / Dissertation (MSc)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MSc / Unrestricted

Due to the influence of information fusion in Industrial Internet of Things (IIoT) environments, there are many problems, such as weak intelligent visual target positioning, disappearing features, large error in visual positioning processes, and so on. Therefore, this paper proposes a weak target positioning method based on multi-information fusion, namely the “confidence interval method”. The basic idea is to treat the brightness and gray value of the target feature image area as a population with a certain average and standard deviation in IIoT environments. Based on the average and the standard deviation, and using a reasonable confidence level, a critical threshold is obtained. Compared with the threshold obtained by the maximum variance method, the obtained threshold is more suitable for the segmentation of key image features in an environment in which interference is present. After interpolation and de-noising, it is applied to mobile weak target location of complex IIoT systems. Using the metallurgical industry for experimental analysis, results show that the proposed method has better performance and stronger feature resolution.

With the developments of industry 4.0, data analytics solutions and their applications have become more prevalent in the manufacturing industry. Currently, the typical software architecture supporting these solutions is modular, using separate software for data collection, storage, analytics, and visualization. The integration and maintenance of such a solution requires the expertise of an information technology team, making implementation more challenging for small manufacturing enterprises. To allow small manufacturing enterprises to more easily obtain the benefits of industry 4.0 data analytics, a full-stack data analytics framework is presented and its performance evaluated as applied in the common industrial analytics scenario of predictive maintenance. The predictive maintenance approach was achieved by using a full-stack data analytics framework, comprised of the PTC Thingworx software suite. When deployed on a lab-scale factory, there was a significant increase in factory uptime in comparison with both preventative and reactive maintenance approaches. The predictive maintenance approach simultaneously eliminated unexpected breakdowns and extended the uptime periods of the lab-scale factory. This research concluded that similar or better results may be obtained in actual factory settings, since the only source of error on predictions would not be present in real world scenarios.

The manufacturing industry is continuously evolving as new practices and technology are adopted to improve productivity and remain competitive. There have been three well established manufacturing revolutions in recent history and some say that the fourth is occurring currently by the name of Smart Manufacturing, Indusrie 4.0, and others. This latest manufacturing revolution is highly dependent on industrial connectivity. This research aims to gage the ability of Industrial Innovation Platforms (IIPs) to reduce complexity of implementing base-line industrial connectivity for small and medium-sized enterprises (SMEs). The results of this study would be especially relevant to decision makers in industrial SMEs who are considering implementing industrial connectivity as well as providing insights into approaches for establishing base-line industrial connectivity. The research methodology consists of three main steps: 1) creation of IIP and non-IIP connectivity solutions that enable connectivity of the vast amount of industrial equipment, 2) Gathering measures from solutions in accordance with metrics identified for complexity evaluation, 3) discussion and interpretation of data To have a more complete analysis, quantitative and qualitative data was used and evaluated to address the varying elements of the broad task of establishing industrial connectivity. The research showed that IIPs can reduce complexity for select industrial equipment. Some industrial equipment have robust and streamlined connectivity solutions provided by the IIP. In these cases, the IIP almost certainly will reduce the complexity of establishing connectivity. Other industrial equipment have a solution provided by the IIP which requires piecing together and some component modifications. In these cases, the IIPs reduce complexity of establishing connectivity dependent on circumstances. Lastly, when no form of solution is available through the IIP for the industrial equipment, the IIP's has no ability to reduce complexity other than hosting the server used in connectivity. These findings open additional avenues of research which could improve the understanding of benefits IIPs may provide to SMEs.

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last thirty years the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company and software tools have been developed by PTC Inc. to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo Cyber-Physical Lab, an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.

I detta examensarbete analyseras Purduemodellen och dess tillämplighet inomnätverkssäkerhet för industriella styrsystem inom ramarna för Industri 4.0. Genomen litteraturstudie granskas modellens struktur och funktion i relation till de nyautmaningarna som uppkommit genom ökad digitalisering och integrering av IIoTteknologier. Studien identifierar både styrkor och svagheter i den traditionellaPurduemodellen. I resultatavsnittet introduceras en modifierad version avPurduemodellen, utformad för att förstärka nätverkssäkerheten och öka systemensförmåga att hantera cyberhot samt anpassa sig till teknologiska förändringar i denindustriella sektorn. Denna anpassning har genomförts genom införandet avytterligare säkerhetsstandarder och verktyg i syfte att förbättra modellenseffektivitet och relevans. / In this thesis, the Purdue model and its applicability within network security forindustrial control systems under the framework of Industry 4.0 are analyzed.Through a literature review, the model's structure and function are examined inrelation to the new challenges that have emerged due to increased digitization andintegration of IIoT technologies. The study identifies both strengths and weaknessesin the traditional Purdue model. In the results section, a modified version of thePurdue model is introduced, designed to enhance network security and increase thesystems' ability to handle cyber threats and adapt to technological changes in theindustrial sector. This adaptation has been achieved by incorporating additionalsecurity standards and tools aimed at improving the model's efficiency andrelevance.

Today the digital evolution is progressing rapidly. This entails both pros and cons concerning the security of devices. Despite the evolution, security has been left in the dark. This results in threats and vulnerabilities in devices, which could potentially be used by a hacker with the purpose of exploiting information. Security has not been a priority in industrial enterprises, even though industrial devices and other networked devices reside on the same network. The evolution of the infrastructure of the Internet has resulted in an increase of cyberattacks. These attacks used to target random individuals. The attacks of today are more intelligent, and hackers have changed their targets to specific enterprises to further exploit sensitive information, damage devices or for financial benefits. Safety in today’s industrial workplaces, such as firewalls, encryption and intrusion detection systems are not specifically designed to work in this type of environment. This leads to new threats and vulnerabilities which further leads to more exploited vulnerabilities. This formulate the following questions: Which are the most occurring threats and vulnerabilities today? What current methods and tools are suited for controlling security in IIoT-networks and its internal industrial devices? The purpose of this thesis was to examine the most occurring threats and vulnerabilities in IIoT-networks and its internal devices and reason among the methods to evaluate security in industrial enterprises. Lastly, an experiment in a real industrial workplace was conducted to attain a nuanced picture of the implementation of finding threats and vulnerabilities in industrial systems. In summary, there are a lot of different threats and vulnerabilities divided into categories and many tools are available to ensure the vulnerability. To conduct a test to find threats and vulnerabilities in an industrial enterprise, it needs to be ethically correct and the consequences carefully considered. The result of this thesis is a mapping and a demonstration of how threats and vulnerabilities are detected in an industrial workplace.

<p>U disertaciji je predstavljeno istraživanje usmereno ka problemu unapređenja rada postojećih industrijskih SCADA sistema. U fokus istraživanja su postavljeni zadaci klasičnih SCADA sistema kao što su: prikupljanje podataka o postrojenju, skladištenje i analiza podataka, kao i prikaz podataka o postrojenju i na koji način se ovi zadaci mogu unaprediti kroz primenu postojećih mobilnih tehnologija, tehnologija Interneta stvari, kao i tehnologija proširene realnosti. Kao rezultat istraživanja predložen je novi model mobilnih nadzornih sistema sa proširenom realnošću i integrisanim industrijskim Internetom stvari. Predloženi model omogućava realizaciju novih sistema nadzora industrijskih postrojenja baziranih na Industriji 4.0 kao i jednostavnu integraciju u postojeće industrijske sisteme. Predloženi novi model nadzornih sistema je opšti model i njegova velika prednost je univerzalnost i otvorenost za primenu novih mobilnih tehnologija, tehnologija Interneta stvari, kao i tehnologija proširene realnosti. On takođe ima svojsvo fleksibilnosti zato što se može primeniti za različite strukture preduzeća, kao i različite proizvodne procese. Predloženi novi model omogućava sledljivost prikupljenih podataka o industrijskim postrojenjima i proizvodnim procesima dvosmerno, od senzora i aktuatora ka menadžmentu postrojenja, kao i od menadžmenta postrojenja ka senzorima i aktuatorima. Omogućena je implementacija različitih algoritama obrade podataka, nezavisnih od predloženog novog modela nadzornih sistema, sa ciljem detektovanja grešaka ili potencijalnih problema u radu industrijskog postrojenja. Predloženi model omogućava uvid u podatke o postrojenju ne samo licima zaduženim za nadzor i održavanje postrojenja, već i proizvođačima industrijske opreme koji dobijaju informacije o opremi u realnim uslovima omogućavajući unapređenje kvaliteta industrijske opreme. Za analizu i obradu podataka prikupljnih o industrijskom postrojenju i procesu model omogućava implementaciju više algoritama istovremeno, čime je moguće međusobno porediti algoritme sa različitim parametrima obrade podataka.</p> / <p>In this doctoral dissertation, a research oriented to the problem of improvement<br />existing industrial SCADA systems is presented. The research is focused on<br />the tasks of classical SCADA systems such as data acquisition, data storage<br />and analysis, and displays of acquired plant data and how these tasks can be<br />improved by applying existing mobile technologies, Internet technology, and<br />technology of augmented reality. As a result of the research, a new model of<br />mobile supervision systems with augmented reality and integrated industrial<br />Internet of Things was proposed. The proposed model enables the<br />implementation of new supervision systems in industrial plants based on<br />Industry 4.0 concept, as well as simple integration into existing industrial plants.<br />The proposed new model of supervision systems is a general model and its<br />great advantage is the universality and openness to the application of new<br />mobile technologies, the Internet of Things technology, and technology of<br />augmented reality. It also has its own flexibility because it can be applied to<br />different company structures and different production processes. The proposed<br />new model enables the traceability of collected data on industrial plants and<br />production processes in two ways, from sensors and actuators to plant<br />management, as well from plant management to sensors and actuators. It is<br />possible to implement different data processing algorithms, independent of the<br />proposed new model of control systems, with the aim of detecting errors or<br />potential problems in the operation of industrial plants or production processes.<br />The proposed model provides insight into plant data not only for plant<br />monitoring and maintenance personnel but also for industrial equipment<br />manufacturers who receive information on equipment in real-world conditions,<br />enabling improvement of the quality of industrial equipment. For the analysis<br />and processing of data collected on industrial plants and production processes,<br />the proposed model enables the implementation of several algorithms at the<br />same time, thus making it possible to compare different algorithms with different<br />data processing parameters.</p>

This thesis details the design of a protocol translator between the industrial-automation protocol OPC UA, and HTTP. The design is based on the architecture of the protocol translator of the Arrowhead framework, and is interoperable with all of its associated protocols. The design requirements are defined to comply with a service-oriented architecture (SOA) and RESTful interaction through HTTP, with minimal requirement of the consuming client to be familiar with OPC UA semantics. Effort is put into making translation as transparent as possible, but limits the scope of this work to exclude a complete semantic translation. The solution presented in this thesis satisfies structural- and foundational interoperability, and bridges interaction to be independent of OPC UA services. The resulting translator is capable of accessing the content of any OPC UA server with simple HTTP-requests, where addressing is oriented around OPC UA nodes.

The use of Internet-of-Things (IoT) makes it possible to inter-connect Information Technology (IT) and Operational Technology (OT) into a completely new system. This convergence is often known as Industrial IoT (IIoT). IIoT brings a lot of benefits to industrial assets, such as improved efficiency and productivity, reduced cost, and depletion of human error. However, the high inter-connectivity opens new possibilities for cyber incidents. These incidents can cause major damage like halting of production on the manufacturing line, or catastrophic havoc to companies, communities, and countries causing power outages, floods, and fuel shortages. Such incidents are important to be predicted, stopped, or alleviated at no cost. Moreover, these incidents are a great motive for researchers and practitioners to investigate known security problems and find potential moderation strategies.  In this thesis work, we try to identify what types of IIoT systems have been investigated in the literature. We seek out to find if software-related issues can yield security problems. Also, we make an effort to perceive what are the proposed methods to mitigate the security threats.We employ the systematic literature review (SLR) methodology to collect this information. The results are gathered from papers published in the last five years and they show an increased interest in research in this domain. We find out software vulnerabilities are a concern for IIoT systems, mainly firmware vulnerabilities and buffer overflows, and there are a lot of likely attacks that can cause damage, mostly injection and DDoS attacks. There are a lot of different solutions which offer the possibility to stop the identified problems and we summarize them. Furthermore, the research gap considering the update process in these systems and devices, as well as a problem with the unsupervised software supply chain is identified.