The American Multi-modal Energy System: Model Development with Structural and Behavioral Analysis using Hetero-functional Graph Theory

In the 21st century, infrastructure is playing an ever greater role in our daily lives. Presidential Policy Directive 21 emphasizes that infrastructure is critical to public confidence, the nation\u27s safety, and its well-being. With global climate change demanding a host of changes across at least four critical energy infrastructures: the electric grid, the natural gas system, the oil system, and the coal system, it is imperative to study models of these infrastructures to guide future policies and infrastructure developments. Traditionally these energy systems have been studied independently, usually in their own fields of study. Therefore, infrastructure datasets often lack the structural and dynamic elements to describe the interdependencies with other infrastructures. This thesis refers to the integration of the aforementioned energy infrastructures into a singular system-of-systems within the context of the United States of America as the American Multi-modal Energy System (AMES). This work develops an open-source structural and behavioral model of the AMES using Hetero-functional Graph Theory (HFGT), a data-driven approach, and model-based systems engineering practices in the following steps. First, the HFGT toolbox code is made available on GitHub and advanced to produce HFGs of systems on the scale of the AMES using the languages Python and Julia. Second, the analytical insights that HFGs can provide relative to formal graphs are investigated through structural analysis of the American Electric Power System which demonstrates how HFGs are better equipped to describe changes in system behavior. Third, a reference architecture of the AMES is developed, providing a standardized foundation to develop future models of the AMES. Fourth, the AMES reference architecture is instantiated into a structural model from which structural properties are investigated. Finally, a physically informed Weighted Least Squares Error Hetero-functional Graph State Estimation analysis of the AMES\u27 socio-economic behavior is implemented to investigate the behavior of the AMES with asset level granularity. These steps provide a reproducible and reusable structural and behavioral model of the AMES for guiding future policies and infrastructural developments to critical energy infrastructures

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

E-business Model Innovation and Capability Building

e-commerce, business models, capacity building

Applications of Axiomatic Design in academic publications 2013-2018 : A Systematic Literature Review

Aksiomaattinen suunnitteluteoria on ollut kasvavan kiinnostuksen kohteena tiedeyhteisössä siitä lähtien, kun Nam P. Suh esitteli teorian 1990-luvulla. Siitä huolimatta, että aiheesta on tehty runsaasti aktiivista tutkimusta (muun muassa vuotuinen aksiomaattiseen suunnitteluteoriaan keskittynyt konferenssi), kattavia kirjallisuuskatsauksia on kirjoitettu vähän. Tämä tutkimus pyrkii osaltaan täyttämään edelläkuvattua aukkoa aksiomaattisen suunnitteluteorian tutkimuskentällä, keskittyen julkaisuihin vuodesta 2013 vuoteen 2018. Tutkimus on kirjoitettu jatkumoksi vuonna 2010 tutkijoiden Kulak, Cebi & Kahmaran (2010) julkaisemalle kirjallisuuskatsakuselle. Tämän vuoksi samankaltainen kategorisointi on implementoitu tähän tutkimukseen. Kategorisoinnin perusteina ovat käytetty aksiooma, sovellutusalue, metodologia ja määrittelytyyppi. Sovellutusalueisiin on tässä tutkimuksessa lisätty ’palvelut’ omana, uutena kategorianaan. Työssä esitellään lyhyesti aksiomaattinen suunnitteluteoria ja sen keskeiset osa- alueet, tärkeimpinä sunnnittelualueet, suunnitteluprosessi ja suunnitteluaksioomat. Metodologia-osiossa taustoitetaan systemaattisen kirjallisuuskatsauksen soveltamista tähän tutkimukseen ja kuvataan prosessin toteutus PRISMA-mallia käyttäen. Tutkimustulokset käydään lyhyesti läpi esimerkein kustakin kategoriasta. Tutkimusaineisto esitetään sekä lukuina, liitteenä että graaffeina. Näitä kirjallisuuskatsauksen tutkimustuloksia verrataan varhemman tutkimuksen vastaaviin. Sovelletun aksiooman suhteen merkittäviä muutoksia ei ole havaittavissa tämän tutkimuksen perusteella aikaisempaan kirjallisuuskatsaukseen verrattuna. Sovellutusaluessa, sitä vastoin, systeemisuunnittelun osuus on kasvanut merkittävästi edelliseen tutkimukseen verrattuna, kun taas ohjelmistosuunnittelun osuus on vastaavasti pienentynyt. Palvelusuunnittelun osuus on verrattain vaatimaton, joskin suurempi kuin esimerkiksi ohjelmistosuunnittelun. Tämän tutkimuksen perusteella suositellaan jatkotutkimuksia erityisesti aksiomaattisen suunitteluteorian sovellutuksista ohjelmisto- ja palvelusuunnitteluihin sekä mahdollisista syistä, miksi mainittujen sovellutusaljen osuus tutkimuskentässä on pienehkö.Axiomatic Design theory has been gaining growing interest within a design community since introduced by Nam P. Suh in 1990s. Despite an active research within the field, including yearly international conferences of Axiomatic Design, there is not many literature reviews made of academic publications of Axiomatic Design. This research aims to fulfil previously described gap on secondary research of Axiomatic Design, focusing on papers published between 2013 and 2018. The study is a continuum to previous paper published by Kulak, Cebi & Kahmaran (2010). Therefore, similar categorization based on applied axiom, application area, methodology and evaluation type is applied in this research. In application area, ‘services’ have been added as a new category for this research. Results are compared with previous study. In findings, no siginificant change has happened in applied axiom, Independece axiom still being clearly more popular within academic publications. In application area, however, there are noticeable changes compared to the previous study. Share of system design has increased while softare design has significantly decreased. Future research is recommended to further explore specifically applications of Axiomatic Design in software and in services

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.Comment: 33 pages, 13 figure

Multiple security domain nondeducibility in cyber-physical systems

Cyber-physical Systems (CPS) present special problems for security. This dissertation examines the cyber security problem, the physical security problem, the security problems presented when cyber systems and physical systems are intertwined, and problems presented by the fact that CPS leak information simply by being observed. The issues presented by applying traditional cyber security to CPS are explored and some of the shortcomings of these models are noted. Specific models of a drive-by-wire\u27\u27 automobile connected to a road side assistance network, a Stuxnet type\u27\u27 attack, the smart grid, and others are presented in detail. The lack of good tools for CPS security is addressed in part by the introduction of a new model, Multiple Security Domains Nondeducibility over an Event System, or MSDND(ES). The drive-by-wire automobile is studied to show how MSDND(ES) is applied to a system that traditional security models do not describe well. The issue of human trust in inherently vulnerable CPS with embedded cyber monitors, is also explored. A Stuxnet type attack on a CPS is examined using both MSDND(ES) and Belief, Information acquisition, and Trust (BIT) logic to provide a clear and precise method to discuss issues of trust and belief in monitors and electronic reports. To show these techniques, the electrical smart grid as envisioned by the Future Renewable Electric Energy Delivery and Management Systems Center (FREEDM) project is also modeled. Areas that may lead to the development of additional tools are presented as possible future work to address the fact: CPS are different and require different models and tools to understand. --Abstract, page iii

Security Analysis of System Behaviour - From "Security by Design" to "Security at Runtime" -

The Internet today provides the environment for novel applications and processes which may evolve way beyond pre-planned scope and purpose. Security analysis is growing in complexity with the increase in functionality, connectivity, and dynamics of current electronic business processes. Technical processes within critical infrastructures also have to cope with these developments. To tackle the complexity of the security analysis, the application of models is becoming standard practice. However, model-based support for security analysis is not only needed in pre-operational phases but also during process execution, in order to provide situational security awareness at runtime. This cumulative thesis provides three major contributions to modelling methodology. Firstly, this thesis provides an approach for model-based analysis and verification of security and safety properties in order to support fault prevention and fault removal in system design or redesign. Furthermore, some construction principles for the design of well-behaved scalable systems are given. The second topic is the analysis of the exposition of vulnerabilities in the software components of networked systems to exploitation by internal or external threats. This kind of fault forecasting allows the security assessment of alternative system configurations and security policies. Validation and deployment of security policies that minimise the attack surface can now improve fault tolerance and mitigate the impact of successful attacks. Thirdly, the approach is extended to runtime applicability. An observing system monitors an event stream from the observed system with the aim to detect faults - deviations from the specified behaviour or security compliance violations - at runtime. Furthermore, knowledge about the expected behaviour given by an operational model is used to predict faults in the near future. Building on this, a holistic security management strategy is proposed. The architecture of the observing system is described and the applicability of model-based security analysis at runtime is demonstrated utilising processes from several industrial scenarios. The results of this cumulative thesis are provided by 19 selected peer-reviewed papers