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PROFINET Security: A Look on Selected Concepts for Secure Communication in the Automation Domain
(2023)
We provide a brief overview of the cryptographic security extensions for PROFINET, as defined and specified by PROFIBUS & PROFINET International (PI). These come in three hierarchically defined Security Classes, called Security Class 1, 2 and 3. Security Class 1 provides basic security improvements with moderate implementation impact on PROFINET components. Security Classes 2 and 3, in contrast, introduce an integrated cryptographic protection of PROFINET communication. We first highlight and discuss the security features that the PROFINET specification offers for future PROFINET products. Then, as our main focus, we take a closer look at some of the technical challenges that were faced during the conceptualization and design of Security Class 2 and 3 features. In particular, we elaborate on how secure application relations between PROFINET components are established and how a disruption-free availability of a secure communication channel is guaranteed despite the need to refresh cryptographic keys regularly. The authors are members of the PI Working Group CB/PG10 Security.
Context: Higher education is changing at an accelerating pace due to the widespread use of digital teaching and emerging technologies. In particular, AI assistants such as ChatGPT pose significant challenges for higher education institutions because they bring change to several areas, such as learning assessments or learning experiences.
Objective: Our objective is to discuss the impact of AI assistants in the context of higher education, outline possible changes to the context, and present recommendations for adapting to change.
Method: We review related work and develop a conceptual structure that visualizes the role of AI assistants in higher education.
Results: The conceptual structure distinguishes between humans, learning, organization, and disruptor, which guides our discussion regarding the implications of AI assistant usage in higher education. The discussion is based on evidence from related literature.
Conclusion: AI assistants will change the context of higher education in a disruptive manner, and the tipping point for this transformation has already been reached. It is in our hands to shape this transformation.
The trend towards the use of Ethernet in automation networks is ongoing. Due to its high flexibility, speed, and bandwidth, Ethernet nowadays is not only widely used in homes and offices worldwide but finding its way into industrial applications. Especially in automation processes, where many field devices send data in relative short time spans, the requirements for a safe and fast data transfer are high. This makes the use of industrial Ethernet essential. A new hardware-layer, specifically tailored for industrial applications, has been introduced in the form of Ethernet-APL (‘Advanced Physical Layer’). Ethernet-APL is based on the Ethernet standard and implements a two-wire Ethernet-based communication for field devices and provides data and power over a two-wire cable. The operation in areas with potentially explosive atmosphere is also possible. This enables a modular, fast, and transparent Ethernet network structure throughout the entire plant. However, by integrating Ethernet-APL into the field, industrial networks in the future will face the challenge of operating at varying datarates at different locations in the network, resulting in a ‘mixed link speed’ network. This can lead to limitations in packet-throughput and consequently to potential packet loss of system relevant data, which must be avoided. Therefore, the purpose of this thesis is to investigate the potential of packet loss in ‘mixed link speed’ networks.
In this paper we describe the selection of a modern build automation tool for an industry research partner of ours, namely an insurance company. Build automation has become increasingly important over the years. Today, build automation became one of the central concepts in topics such as cloud native development based on microservices and DevOps. Since more and more products for build automation have entered the market and existing tools have changed their functional scope, there is nowadays a large number of tools on the market that differ greatly in their functional scope. Based on requirements from our partner company, a build server analysis was conducted. This paper presents our analysis requirements, a detailed look at one of the examined tools and a summarizes our comparison of all three tools from our final comparison round.
Music streaming platforms offer music listeners an overwhelming choice of music. Therefore, users of streaming platforms need the support of music recommendation systems to find music that suits their personal taste. Currently, a new class of recommender systems based on knowledge graph embeddings promises to improve the quality of recommendations, in particular to provide diverse and novel recommendations. This paper investigates how knowledge graph embeddings can improve music recommendations. First, it is shown how a collaborative knowledge graph can be derived from open music data sources. Based on this knowledge graph, the music recommender system EARS (knowledge graph Embedding-based Artist Recommender System) is presented in detail, with particular emphasis on recommendation diversity and explainability. Finally, a comprehensive evaluation with real-world data is conducted, comparing of different embeddings and investigating the influence of different types of knowledge.
Die Angriffserkennung ist ein wesentlicher Bestandteil, Cyberangriffe zu verhindern und abzumildern. Dazu werden Daten aus verschiedenen Quellen gesammelt und auf Einbruchsspuren durchsucht. Die heutzutage produzierten Datenmengen sind ein wesentliches Problem für die Angriffserkennung. Besonders bei komplexen Cyberangriffen, die über einen längeren Zeitraum stattfinden, wächst die zu durchsuchende Datenmenge stark an und erschwert das Finden und Kombinieren der einzelnen Angriffsschritte.
Eine mögliche Lösung, um dem Problem entgegenzuwirken, ist die Reduktion der Datenmenge. Die Datenreduktion versucht, Daten herauszufiltern, die aus Sicht der Angriffserkennung irrelevant sind. Diese Ansätze werden unter dem Begriff Reduktionstechniken zusammengefasst. In dieser Arbeit werden Reduktionstechniken aus der Wissenschaft untersucht und auf Benchmark Datensätzen angewendet, um ihre Nutzbarkeit zu evaluieren. Dabei wird der Frage nachgegangen, ob die Reduktionstechniken in der Lage sind, irrelevante Daten ausfindig zu machen und zu reduzieren, ohne dass eine Beeinträchtigung der Angriffserkennung stattfindet. Die Evaluation der Angriffserkennung erfolgt durch ThreaTrace, welches eine Graph Neural Network basierte Methode ist.
Die Evaluierung zeigt, dass mehrere Reduktionstechniken die Datenmenge wesentlich reduzieren können, ohne die Angriffserkennung zu beeinträchtigen. Bei drei Techniken führt der Einsatz zu keinen nennenswerten Veränderungen der Erkennungsraten. Dabei wurden Reduktionsraten von bis zu 30 % erreicht. Bei der Anwendung einer Reduktionstechnik stieg die Erkennungsleistung sogar um 8 %. Lediglich bei zwei Techniken führt der Einsatz zum drastischen Absinken der Erkennungsrate.
Insgesamt zeigt die Arbeit, dass eine Datenreduktion angewandt werden kann, ohne die Angriffserkennung zu beeinträchtigen. In besonderen Fällen kann eine Datenreduktion, die Erkennungsleistung sogar verbessern. Allerdings ist der erfolgreiche Einsatz der Reduktionstechniken abhängig vom verwendeten Datensatz und der verwendeten Methode der Angriffserkennung.
The transfer of historically grown monolithic software architectures into modern service-oriented architectures creates a lot of loose coupling points. This can lead to an unforeseen system behavior and can significantly impede those continuous modernization processes, since it is not clear where bottlenecks in a system arise. It is therefore necessary to monitor such modernization processes with an adaptive monitoring concept to be able to correctly record and interpret unpredictable system dynamics. This contribution presents a generic QoS measurement framework for service-based systems. The framework consists of an XML-based specification for the measurement to be performed – the Information Model (IM) – and the QoS System, which provides an execution platform for the IM. The framework will be applied to a standard business process of the German insurance industry, and the concepts of the IM and their mapping to artifacts of the QoS System will be presented. Furtherm ore, design and implementation of the QoS System’s parser and generator module and the generated artifacts are explained in detail, e.g., event model, agents, measurement module and analyzer module.
Der Bachelor-Studiengang Mediendesigninformatik der Hochschule Hannover ist ein Informatikstudiengang mit dem speziellen Anwendungsgebiet Mediendesign. In Abgrenzung von Studiengängen der Medieninformatik liegt der Anwendungsfokus auf der kreativen Gestaltung etwa von 3D-Modellierungen, Animationen und Computerspielen. Absolvent*innen des Studiengangs sollen an der Schnittstelle zwischen Informatik und Mediendesign agieren können, zum Beispiel bei der Erstellung von Benutzungsschnittstellen und VR/AR-Anwendungen. Der Artikel stellt das Curriculum des interdisziplinären Studiengangs vor und reflektiert nach dem Abschluss der ersten beiden Studierendenkohorten die Erfahrungen, indem die ursprünglichen Ziele den Zahlen der Hochschulstatistik und den Ergebnissen zweier Studierendenbefragungen gegenübergestellt werden.
Digital data on tangible and intangible cultural assets is an essential part of daily life, communication and experience. It has a lasting influence on the perception of cultural identity as well as on the interactions between research, the cultural economy and society. Throughout the last three decades, many cultural heritage institutions have contributed a wealth of digital representations of cultural assets (2D digital reproductions of paintings, sheet music, 3D digital models of sculptures, monuments, rooms, buildings), audio-visual data (music, film, stage performances), and procedural research data such as encoding and annotation formats. The long-term preservation and FAIR availability of research data from the cultural heritage domain is fundamentally important, not only for future academic success in the humanities but also for the cultural identity of individuals and society as a whole. Up to now, no coordinated effort for professional research data management on a national level exists in Germany. NFDI4Culture aims to fill this gap and create a usercentered, research-driven infrastructure that will cover a broad range of research domains from musicology, art history and architecture to performance, theatre, film, and media studies.
The research landscape addressed by the consortium is characterized by strong institutional differentiation. Research units in the consortium's community of interest comprise university institutes, art colleges, academies, galleries, libraries, archives and museums. This diverse landscape is also characterized by an abundance of research objects, methodologies and a great potential for data-driven research. In a unique effort carried out by the applicant and co-applicants of this proposal and ten academic societies, this community is interconnected for the first time through a federated approach that is ideally suited to the needs of the participating researchers. To promote collaboration within the NFDI, to share knowledge and technology and to provide extensive support for its users have been the guiding principles of the consortium from the beginning and will be at the heart of all workflows and decision-making processes. Thanks to these principles, NFDI4Culture has gathered strong support ranging from individual researchers to highlevel cultural heritage organizations such as the UNESCO, the International Council of Museums, the Open Knowledge Foundation and Wikimedia. On this basis, NFDI4Culture will take innovative measures that promote a cultural change towards a more reflective and sustainable handling of research data and at the same time boost qualification and professionalization in data-driven research in the domain of cultural heritage. This will create a long-lasting impact on science, cultural economy and society as a whole.