TY - THES A1 - Hildebrandt, Hans T1 - Integration von Cyber Threat Intelligence (CTI) in IT- und Medizinumgebungen: Quellen, Formate und praktische Umsetzung zur Verbesserung der IT-Sicherheit N2 - Die vorliegende Arbeit untersucht die Integration von Cyber Threat Intelligence in IT- und Gesundheitsumgebungen. Ziel war es, zu evaluieren, ob CTI-Daten in bestehende Sicherheitslösungen eingebunden werden können. Außerdem sollte festgestellt werden, welchen Mehrwert die Daten im Hinblick auf die Erkennung und Abwehr von Bedrohungen bieten. Hierzu wurde eine Laborumgebung aufgebaut, bestehend aus OpenCTI zur Sammlung und Verwaltung von Bedrohungsinformationen, Logstash zur Datenweiterleitung sowie Wazuh zur Analyse und Alarmierung. Über einen Zeitraum von acht Wochen wurden Daten aus verschiedenen Open-Source-Feeds (u. a. CVE, MITRE, AlienVault, ThreatFox) gesammelt, verarbeitet und auf ihre Relevanz für den Gesundheitssektor geprüft. Zur Bewertung der Qualität der CTI-Daten wurde ein Beispielszenario für eine Krankenhausinfrastruktur entwickelt. Dieses Szenario umfasste typische Systeme und Hersteller im Gesundheitssektor, darunter Krankenhausinformationssysteme, Medizingeräte sowie Netzwerkinfrastrukturen. Die gesammelten Daten wurden auf diese Infrastruktur gemappt, um zu prüfen, in welchem Umfang spezifische Bedrohungen abgedeckt werden. Die Ergebnisse zeigen, dass eine technische Integration grundsätzlich möglich ist und für generische IT-Bedrohungen belastbare Indikatoren vorliegen. Spezifische Informationen für den Gesundheitssektor konnten jedoch nur eingeschränkt identifiziert werden. Daraus ergibt sich, dass die Nutzung spezialisierter Feeds, etwa aus dem Health-ISAC, notwendig ist, um sektorspezifische Bedrohungen zuverlässig abdecken zu können. KW - Computersicherheit KW - Informationssicherheit Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:522-opus4-34377 ER - TY - CHAP A1 - Weich, Patrick A1 - Lobachev, Oleg T1 - PIN-a-Boo: Revealing Smartphone PINs via Segmentation and Hand Skeleton Tracking from Video Feeds T2 - WSCG 2025 Proceedings : Computer Science Research Notes - CSRN N2 - It is crucial to improve smartphone security, given the prevalence of sensitive information stored on them. This study presents an attack strategy that reveals smartphone PIN entries using computer vision and pattern recognition techniques. By leveraging modern segmentation and hand skeleton tracking, our method accurately identifies and analyzes finger movement patterns, even when partially obscured. We can reliably infer the entered PIN by combining these movement patterns with the smartphone’s position and the on-screen keypad layout. This approach significantly enhances shoulder-surfing attacks, requiring only a video recording of the entry process. Our attack requires much less specialized expertise, making it more accessible. We conclude by analyzing the method’s potential impact and its implications for public safety. Y1 - 2025 U6 - https://doi.org/10.24132/CSRN.2025-31 SN - 464-4617 SN - 464-4625 SP - 293 EP - 304 PB - WSCG CY - Plzen ER - TY - CHAP A1 - Henkel, Clara A1 - Bestle, Dieter A1 - Flassig, Peter A1 - Janke, Christian A1 - Slaby, Michal T1 - Consideration of Balancing within the Fan-Blisk Design Process T2 - Proceedings of Global Power and Propulsion Society, Shanghai, 2025 N2 - The design of fan-blisks is a multi-criterion optimisation challenge primarily involving the aerodynamic shape optimisation of blade profiles and subsequent blade balancing, i.e., shifting profiles in axial and circumferential directions to avoid stress hotspots. Although it is already known that blade balancing affects aerodynamic properties, this correlation is often not taken into account, which is why it is usually performed as an independent step after solving the aerodynamic design problem. However, this assumption is questionable because such shifts should be used to control both secondary flow effects and resulting stress. Therefore, the paper proposes problem formulations combining both aspects. Since optimisation requires costly numerical evaluations, machine learning methods are investigated to predict aerodynamic performance and stress constraints more efficiently. This enables a global optimisation process by reducing computational costs. Various different surrogate types are investigated, where stress constraints are formulated either as regression task predicting stress maxima, or as classification problem directly assessing design feasibility. Y1 - 2025 UR - https://gpps.global/wp-content/uploads/2025/09/GPPS-TC-2025_paper_017.pdf U6 - https://doi.org/10.33737/gpps25-tc-017 SN - 2504-4400 SP - 1 EP - 9 PB - GPPS ER - TY - CHAP A1 - Schulz, Theresa A1 - Jander, Kai T1 - Planless Agents: BDI Plan Generation Using LLMs T2 - Intelligent Distributed Computing XVII, 2024 N2 - Belief-Desire-Intention (BDI) agents offer a unique approach for engi- neering complex behavior for individual agents in multi-agent systems. A developer can define goals for each agent, specifying the desired outcomes in various con- texts and implement plans as the means to reach those goals. The BDI reasoning engine can then proceed to automatically select goals to pursue (goal deliberation) and choose one or more of the provided plans to attempt to achieve them. However, usually plans or at least part of the plans have to be provided by the agent’s developer before the system is deployed. In this paper we present an approach for BDI agents to generate their own plans using large language models (LLMs) using solely the available context information such as goal descriptions, available beliefs and their structural information. We show that the approach is viable in principle and explore its reliability as well as discuss further use of LLMs in the context of automating BDI-based agents. Y1 - 2025 UR - https://link.springer.com/chapter/10.1007/978-3-031-87639-4_15 U6 - https://doi.org/10.1007/978-3-031-87639-4_15 IS - 1203 SP - 253 EP - 268 PB - Springer ER - TY - CHAP A1 - Helmich, Roland A1 - Braubach, Lars A1 - Schulz, Theresa A1 - Jander, Kai T1 - Securing End-to-End Encrypted File Sharing Services with the Messaging Layer Security Protocol T2 - Intelligent Distributed Computing XVII, 2024 N2 - To protect data on the servers of cloud service providers, file-sharing services rely on End-to-End Encryption (E2EE). However, existing solutions have weaknesses that allow attackers to bypass E2EE permanently after stealing a clients keys once. In this paper, a concept for an E2EE file-sharing service is proposed which does not have this vulnerability. It is based on Messaging Layer Security (MLS) groups for key distribution, an authentication system based on asymmetric cryptography, Attribute-Based Access Control (ABAC) based access rights and a tamper-proof versioned storage system for synchronising sensitive data. The applicability of the concept is demonstrated by a prototype implementation and an evaluation based on benchmarks and a security analysis. Overall, the concept can fulfil the requirements of a basic file sharing service while providing stronger security guarantees than existing solutions. Y1 - 2025 UR - https://link.springer.com/chapter/10.1007/978-3-031-87639-4_7 U6 - https://doi.org/10.1007/978-3-031-87639-4_7 IS - 1203 SP - 107 EP - 122 PB - Springer ER - TY - CHAP A1 - Frosch, Katharina A1 - Lindauer, Friederike A1 - Winkel, Carmen T1 - Is Learning with an AI-Powered Chatbot for Everyone? A First Look at How Learning Preferences May Influence Learning Outcomes T2 - Ubiquity Proceedings N2 - In an era where AI-powered chatbots are increasingly being integrated into education and corporate learning, it is critical to determine whether these approaches benefit all learners or primarily cater to those with specific preferences. This study explores the interplay between learning preferences and learning outcomes in communication training using an AI-powered chatbot. In a field experiment with 17 participants, systematic thinkers and intrinsically motivated learners reported higher satisfaction and greater skill improvement, while those who preferred model learning and direct feedback benefited less. These findings suggest that AI-powered chatbots should be carefully designed to accommodate diverse learners and mitigate potential negative effects. Y1 - 2025 U6 - https://doi.org/10.5334/uproc.207 VL - 6 IS - 1 SP - 1 EP - 10 PB - ubiquity press ER - TY - CHAP A1 - Bertram, Hamun A1 - Janke, Christian A1 - Flassig, Robert A1 - Flassig, Peter T1 - Efficient ML-Based Prediction of Turbomachinery Blade Performance With B-Spline Surface Representation T2 - ASME Turbo Expo 2025: Turbomachinery Technical Conference and Exposition, Memphis, Tennessee, USA, 2025 N2 - In turbomachinery blade design, rapid and accurate performance prediction is essential to accelerate optimization and reduce reliance on costly high-fidelity simulations. Traditional data-driven approaches often use dense surface point-cloud representations as input features, requiring extensive training datasets and computational resources. This work presents a more efficient methodology leveraging a compact B-Spline-based surface representation, where control points serve as input features, significantly reducing geometric dimensionality and computational overhead. A systematic Design of Experiments (DoE) is performed to generate a diverse set of blade geometries for NASA Rotor 67. Each design is evaluated via computational fluid dynamics (CFD) simulations in ANSYS CFX, providing key aerodynamic performance metrics such as isentropic efficiency. We train and compare Graph Convolutional Neural Networks (GCNN) and Random Forest Regression (RFR) models to predict blade performance directly from the reduced control-point parameterization. Incorporating first- and second-order geometric derivatives (gradients and Laplacians) into the feature set significantly enhances predictive accuracy and stability, capturing essential curvature-related flow physics. Results demonstrate that this B-Spline-based, CAD-centric methodology can achieve competitive accuracy in predictions with as few as 150–200 training simulations—comparable to other GCNN-based approaches. Consequently, the proposed framework reduces training overhead from days to minutes, enabling faster, more cost-effective turbomachinery design workflows and guiding optimization toward high-performing blade geometries. Y1 - 2025 SN - 978-0-7918-8886-5 U6 - https://doi.org/10.1115/GT2025-152689 PB - ASME ER - TY - CHAP A1 - Müller, Katja A1 - Markgraf, Klaus A1 - Vogel, Andreas A1 - Flassig, Peter A1 - Flassig, Robert T1 - Numerical Study of Ice Accretion on Fan Blades: Implications for the Design of Blade Geometries T2 - ASME Turbo Expo 2025: Turbomachinery Technical Conference and Exposition, Memphis, Tennessee, USA, 2025 N2 - Ice formation on aircraft components due to the impact of supercooled droplets poses a severe safety risk. In particular, the formation of ice on the fan blades can lead to vibrations that affect the entire engine. While numerous studies have examined the effects of environmental conditions on ice accumulation, the influence of blade geometry has received little attention. This study investigates how variations in blade geometry affect ice accretion in a low-pressure compressor using a numerical approach. A Design of Experiments (DoE) is conducted on the NASA Rotor67, focusing on the sensitivity of ice formation to geometric modifications. The workflow includes geometry generation (ParaBlade), flow simulation (ANSYS CFX), and ice accretion modeling (ANSYS FENSAP-ICE) under rime ice conditions. The results reveal a strong correlation between the inlet metal angle and both accreted ice mass and maximum ice thickness. Furthermore, designs with good aerodynamic performance tend to exhibit higher ice accumulation. These findings enhance the understanding of icing behavior in low-pressure compressors and offer valuable insights for optimizing blade design in adverse environmental conditions. Y1 - 2025 SN - 978-0-7918-8887-2 U6 - https://doi.org/10.1115/GT2025-152704 PB - ASME ER - TY - CHAP A1 - Nitze, André A1 - Wang, Tingting A1 - Jahn, Josephine A1 - Ali, Sabah T1 - Beyond Connectivity: A Sustainable Approach to Municipal LoRaWAN Infrastructure and Services T2 - Konferenz: ICDS 2024 : The Eighteenth International Conference on Digital Society; Barcelona, Spain N2 - This paper introduces the LoRaWAN Collaboration Framework (LCF), a strategic blueprint for deploying and managing LoRaWAN infrastructures in smart cities with an emphasis on rural and small municipalities. LoRaWAN technology is distinguished by its capability to support long-range, low-power IoT applications, making it ideal for extensive and sparsely populated areas. The LCF aims to address common challenges in these settings, such as limited technical expertise, financial constraints, and the need for cross-municipal cooperation. It outlines roles and responsibilities across various stakeholders including municipal authorities, IT service providers, application developers, and end-users. The framework emphasizes the balance of technological, economic, ecological, and social sustainability in line with the United Nations' Sustainable Development Goals. In this paper we describe the experiences from several LoRaWAN projects in small towns and municipalities, the derived framework, and future research directions towards ensuring the economic viability of the proposed model. Y1 - 2024 SP - 19 EP - 24 PB - IARIA ER - TY - INPR A1 - Nitze, André T1 - Future-proofing Education: A Prototype for Simulating Oral Examinations Using Large Language Models N2 - This study explores the impact of Large Language Models (LLMs) in higher education, focusing on an automated oral examination simulation using a prototype. The design considerations of the prototype are described, and the system is evaluated with a select group of educators and students. Technical and pedagogical observations are discussed. The prototype proved to be effective in simulating oral exams, providing personalized feedback, and streamlining educators' workloads. The promising results of the prototype show the potential for LLMs in democratizing education, inclusion of diverse student populations, and improvement of teaching quality and efficiency. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2401.06160 SP - 1 EP - 6 PB - arXiv ER - TY - CHAP A1 - Wang, Tingting A1 - Nitze, André A1 - Hofstedt, Petra A1 - Löffler, Sven A1 - Hennig, Silvia A1 - Klinge, Alexander T1 - Technische Machbarkeit von ländlichem On-Demand-Verkehr: Anforderungen an eine Open-Source-Software-Architektur T2 - 24. Nachwuchswissenschaftler:innenkonferenz N2 - On-Demand-Verkehr bezeichnet Mobilitätsangebote, die auf Bestellung verfügbar sind. Ziel ist es, durch flexible Mobilitätsangebote Lücken im ländlichen öffentlichen Personennahverkehr (ÖPNV) zu schließen, insbesondere in dünn besiedelten Räumen und zu Randzeiten des Tages. Die Einführung solcher Dienste gestaltet sich für viele Kommunen jedoch aufgrund der hohen Lizenzgebühren für proprietäre Software als äußerst schwierig, insbesondere in strukturschwachen Regionen, die solche Dienste besonders benötigen. Im Rahmen des Projekts "Open-Source-Software für ländlichen On-Demand-Verkehr" (OSLO) wurde die Umsetzbarkeit einer niedrigschwelligen Open-Source-Lösung geprüft. Diese Lösung soll langfristig in die Open-Source-Mobilitätsplattform bbnavi integriert werden, um die Nutzung vorhandener Mobilitätsdaten zu erleichtern, Interoperabilität zu gewährleisten und die Anpassung an andere Regionen zu vereinfachen. Das Konzept beinhaltet eine Software-Architektur, einen speziell für den ländlichen Nahverkehr entwickelten intermodalen Routing-Algorithmus und ein Betriebs- und Organisationsmodell ([1]). In diesem Beitrag wird beschrieben, wie eine effiziente Software-Architektur für das OSLO-Projekt entworfen wurde. Die zentralen Themen beinhalten die Strukturierung der Komponenten, die Anwendung von GTFS-Flex-V2, die Integration des angepassten Routing-Algorithmus sowie die Einbindung externer Schnittstellen. KW - On-Demand-Verkehr KW - Software-Architektur KW - Open Source Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:mit1-opus4-154308 SN - 1437-7624 IS - 3 SP - 361 EP - 366 PB - Hochschule Mittweida CY - Mittweida ER - TY - JOUR A1 - Nitze, André A1 - Wang, Tingting A1 - Jahn, Josephine A1 - Ali, Sabah A1 - Miesner, Timon T1 - Lessons learned from building sustainable municipal LoRaWan infrastructure JF - International Journal On Advances in Networks and Services N2 - Abstract—This paper introduces the LoRaWAN Collaboration Framework (LCF), a strategic blueprint for deploying and managing LoRaWAN infrastructures in smart cities with an emphasis on rural and small municipalities. LoRaWAN technology distinguishes itself by its capability to support long-range, low-power IoT applications, making it ideal for extensive and sparsely populated areas. The LCF aims to address common challenges in these settings, such as limited technical expertise, financial constraints, and the need for cross-municipal cooperation. It outlines roles and responsibilities across various stakeholders including municipal authorities, IT service providers, application developers, and end-users. The framework emphasizes the balance of technological, economic, social and ecological sustainability in line with the United Nations' Sustainable Development Goals. In this paper, we describe the experiences from several LoRaWAN projects in small towns and municipalities in Germany and give some insights to these use cases, the derived collaboration framework, and other arguments to consider before implementing LoRaWAN infrastructures. Y1 - 2024 VL - 17 IS - 3&4 SP - 59 EP - 68 PB - IARIA ER - TY - JOUR A1 - Pilgermann, Michael A1 - Stein, Stefan A1 - Schrader, Thomas A1 - Weber, Simon T1 - Angriffserkennung beim Betrieb von KRITIS gemäß IT-Sicherheitsgesetz 2.0 JF - Datenschutz und Datensicherheit N2 - Die Krankenhaus-IT sieht sich einer stetig zunehmenden Bedrohung der Sicherheit der Patientendaten und des Krankenhausbetriebes gegenüber. Der Gesundheitssektor gehört zu den Kritischen Infrastrukturen, die zunehmend strengeren Regelungen unterliegen. Jüngste Regulierung ist das IT-Sicherheitsgesetz 2.0, das unter anderem Maßnahmen zur Detektion von Angriffen fordert. Der Beitrag stellt vor, wie eine branchenspezifische Umsetzung dieser Anforderung aussehen könnte. Y1 - 2021 U6 - https://doi.org/10.1007/s11623-021-1525-z VL - 45 SP - 733 EP - 737 ER - TY - JOUR A1 - Stein, Stefan A1 - Pilgermann, Michael A1 - Sedlmayr, Martin T1 - Systematic Evaluation of Manufacturer Disclosure Statements for Medical Device Security (MDS2) to Strengthen Hospital OT Security Measures – Lessons Learned JF - Studies in health technology and informatics N2 - The growing number of connected medical devices in hospitals poses serious operational technology (OT) security challenges. Effective countermeasures require a structured analysis of the communication interfaces and security configurations of individual devices. State of the art: Although Manufacturer Disclosure Statements for Medical Device Security (MDS2, Version 2019) offer relevant information, they are rarely integrated into cybersecurity workflows. Existing studies are limited in scope and lack scalable methodologies for systematic evaluation. Concept: This study analyzed 209 MDS2 documents and 161 security white papers to extract structured information on ports, protocols, and protective measures. Over 52,000 question–answer pairs were converted into a machine-readable format using customized parsing and validation routines. The aim was to establish whether this dataset could inform risk assessments and future applications involving Large Language Models (LLMs). Implementation: The analysis revealed 367 distinct ports, including common protocols such as HTTPS (443), DICOM (104), and RDP (3389), as well as vendor-specific proprietary ports. Approximately 40% of the devices used over 20 ports, indicating a broad attack surface. OCR errors and inconsistent formatting required manual corrections. A consolidated dataset was developed to support clustering, comparison across vendors and versions, and preparation for downstream LLM use, particularly via structured SBOM and configuration data. Lessons learned: Although no model training was conducted, the structured dataset can support AI-based OT security workflows. The findings highlight the critical need for up-to-date, machine-readable manufacturer data in standardized formats and schemas. Such information could greatly enhance the automation, comparability, and scalability of hospital cybersecurity measures. Y1 - 2025 U6 - https://doi.org/10.3233/SHTI251404 VL - 331 SP - 256 EP - 264 ER - TY - JOUR A1 - Weber, Simon B. A1 - Stein, Stefan A1 - Pilgermann, Michael A1 - Schrader, Thomas T1 - Attack Detection for Medical Cyber-Physical Systems – A Systematic Literature Review JF - IEEE Access N2 - The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain’s unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected. Y1 - 2023 U6 - https://doi.org/10.1109/ACCESS.2023.3270225 IS - 11 SP - 41796 EP - 41815 PB - IEEE ER - TY - JOUR A1 - Jamil, Mahnoor A1 - Mihajloska Trpcheska, Hristina A1 - Popovska-Mitrovikj, Aleksandra A1 - Dimitrova, Vesna A1 - Creutzburg, Reiner T1 - Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis JF - Applied Sciences N2 - Image-based spam poses a significant challenge for traditional text-based filters, as malicious content is often embedded within images to bypass keyword detection techniques. This study investigates and compares the performance of six machine learning models—ResNet50, XGBoost, Logistic Regression, LightGBM, Support Vector Machine (SVM), and VGG16—using a curated dataset containing 678 legitimate (ham) and 520 spam images. The novelty of this research lies in its comprehensive side-by-side evaluation of diverse models on the same dataset, using standardized dataset preprocessing, balanced data splits, and validation techniques. Model performance was assessed using evaluation metrics such as accuracy, receiver operating characteristic (ROC) curve, precision, recall, and area under the curve (AUC). The results indicate that ResNet50 achieved the highest classification performance, followed closely by XGBoost and Logistic Regression. This work provides practical insights into the strengths and limitations of traditional, ensemble-based, and deep learning models for image-based spam detection. The findings can support the development of more effective and generalizable spam filtering solutions in multimedia-rich communication platforms. KW - spam detection KW - image spam KW - machine learning KW - Support Vector Machine KW - XGBoost KW - Logistic Regression KW - ResNet50 KW - LightGBM KW - VGG16 Y1 - 2025 U6 - https://doi.org/10.3390/app15116158 SN - 2076-3417 VL - 15 IS - 11 PB - MDPI ER - TY - CHAP A1 - Craig, Kevin A1 - Fitz, Lukas R. G. A1 - Miller, Kasey T1 - IT Identity fostering User Innovation on Digital Platforms: The Role of Self-Esteem T2 - AMCIS 2025, 31st Americas Conference on Information Systems, Montréal, Canada, 2025 N2 - Digital platforms can grow by motivating users to explore new ways to use a wider range of affiliated products and services. This work explores the power of IT Identity to motivate such innovative use, through identity's ability to intrinsically motivate behavior. Data from 209 Amazon.com users indicates that IT Identity may cause Trying to Innovate with an IT, mediated by Self-Esteem. Y1 - 2025 UR - https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1114&context=amcis2025 SP - 1 EP - 9 PB - Association for Information Systems ER - TY - INPR A1 - Tuset-Peiro, Pere A1 - Pilgermann, Michael A1 - Pegueroles, Josep A1 - Vilajosana, Xavier T1 - Assessing Cybersecurity of Internet-Facing Medical IT Systems in Germany & Spain Using OSINT Tools N2 - This paper investigates cybersecurity threats in medical IT (Information Technology) systems exposed to the Internet. To that end, we develop a methodology and build a data processing pipeline that allows to gather data from different OSINT (Open Source Intelligence) sources, and processes it to obtain relevant cybersecurity metrics. To validate its operation and usefulness, we apply it to two countries, Germany and Spain, allowing to study the main threats that affect medical IT systems in these countries. Our initial findings reveal that 20% of German hosts and 15% of Spanish hosts tagged as medical devices have at least one CVE (Common Vulnerabilities and Exposures) with a CVSS (Common Vulnerability Scoring System) graded as critical (i.e., value 8 or greater). Moreover, we found that 74% of CVEs found in German hosts are dated from earlier than 2020, whereas for Spanish hosts the percentage is 60%. This indicates that medical IT systems exposed to the Internet are seldom updated, which further increases their exposure to cyberthreats. Based on these initial findings, we finish the paper providing some insights on how to improve cybersecurity of these systems. Y1 - 2025 U6 - https://doi.org/10.20944/preprints202503.1340.v1 SP - 190 EP - 197 ER - TY - BOOK A1 - Kraska, Martin T1 - Engineering Calculations with SMath BT - First Steps N2 - Introduction to SMath Studio covering the topics installation, configuration, creating text and formulas, scientific units, graphics, embedded images. The tutorial takes approximately 2 hours to complete. KW - SMath KW - Software Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:522-opus4-33709 PB - Technische Hochschule Brandenburg CY - Brandenburg an der Havel ER - TY - JOUR A1 - Daupayev, Nursultan A1 - Engel, Christian A1 - Hirsch, Sören T1 - Two-to-One Trigger Mechanism for Event-Based Environmental Sensing JF - Sensors N2 - Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO2), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO2 measurement is activated when the specified T and RH change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and RH signals, the number of CO2 measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO2 measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO2 concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Y1 - 2025 UR - https://www.mdpi.com/1424-8220/25/13/4107 U6 - https://doi.org/10.3390/s25134107 VL - 25 IS - 13 SP - 1 EP - 18 PB - MDPI ER - TY - JOUR A1 - Stein, Stefan A1 - Pilgermann, Michael A1 - Weber, Simon A1 - Sedlmayr, Martin T1 - Leveraging MDS2 and SBOM data for LLM-assisted vulnerability analysis of medical devices JF - Computational and Structural Biotechnology Journal N2 - This study investigated the use of a semi-automated, Retrieval-Augmented Generation (RAG)-based multi-agent architecture to analyze security-relevant data and assemble specialized exploitation paths targeting medical devices. The input dataset comprised device-specific sources, namely, the Manufacturer Disclosure Statement for Medical Device Security (MDS2) documents and Software Bills of Materials (SBOMs), enriched with public vulnerability databases, including Common Vulnerabilities and Exposures (CVE), Known Exploited Vulnerabilities (KEV), and Metasploit exploit records. The objective was to assess whether a modular, Large Language Model (LLM)-driven agent system could autonomously correlate device metadata with known vulnerabilities and existing exploit information to support structured threat modeling. The architecture follows a static RAG design based on predefined prompts and fixed retrieval logic, without autonomous agent planning or dynamic query adaptation. The developed Vulnerability Intelligence for Threat Analysis in Medical Security (VITAMedSec) system operates under human-prompted supervision and successfully synthesizes actionable insights and exploitation paths without requiring manual step-by-step input during execution. Although technically coherent results were obtained under controlled conditions, real-world validation remains a critical avenue for future research. This study further discusses the dual-use implications of such an agent-based framework, its relevance to patient safety in medical device cybersecurity, and the broader applicability of the proposed architecture to other critical infrastructure sectors. These findings emphasize both the technical potential and ethical responsibility for applying semi-automated AI workflows in medical cybersecurity contexts. Y1 - 2025 U6 - https://doi.org/10.1016/j.csbj.2025.07.012 VL - 28 SP - 267 EP - 280 PB - Elsevier ER - TY - JOUR A1 - Kolláth, Zoltán A1 - Hajdu, Tamás A1 - Degen, Tobias A1 - Jechow, Andreas A1 - Sztakovics, János T1 - Conversion between measurement units used for night sky quality assessment with multispectral (RGB) cameras JF - Journal of Quantitative Spectroscopy and Radiative Transfer N2 - In recent years, many studies have shown that light pollution adversely affects wildlife, ecosystems, and human well-being. To assess and mitigate these impacts, it is crucial that measurements of night sky quality are reliable and comparable across sites and instruments. However, the lack of standardised night sky brightness metrology and the use of a wide variety of measurement instruments with varying spectral responsivity and field-specific measurement units hinder meaningful comparison. We collected night sky spectra from 44 nights at dark locations (existing and proposed dark sky parks). Based on this observational dataset, we created a larger random set of spectra. These data served to fit conversion parameters for a wide variety of units. We demonstrate that RGB cameras, when used as multichannel measuring devices, enable the retrieval of measurements that facilitate conversions between different units. Furthermore, even airglow can be quantified from a given measurement, enabling the determination of oxygen and sodium emission line contributions. Since this contribution is not negligible, quantitative measurements of its magnitude are crucial for accurately assessing light pollution at dark-sky sites. Using our spectral measurement database, we constructed the most probable transformation from the cameras’ R, G, and B channel 𝑑𝑠𝑢 values to other units, such as the astronomical Bessel V band magnitudes. The unit conversion formulas provided in this paper are valid for mildly polluted sites (existing and proposed dark sky places), in the 21-22 magV∕arcsec2 range. Y1 - 2025 U6 - https://doi.org/10.1016/j.jqsrt.2025.109636 VL - 347 SP - 1 EP - 10 PB - Elsevier ER - TY - JOUR A1 - Schulz, Theresa A1 - Jander, Kai T1 - Dynamic plan generation with LLMs: automatic execution of abstract BDI-agent goals JF - International Journal of Parallel, Emergent and Distributed Systems N2 - Belief-Desire-Intention (BDI) agents enable complex behavior in multi-agent systems by defining goals and implementing plans to achieve their goals. Typically, developers pre-define plans, which limits adaptability. This paper presents an approach for BDI agents to dynamically generate plans using large language models (LLMs), exploiting contextual information such as goals, beliefs, and structural data. We evaluate the feasibility, reliability, and limitations of this method, and discuss its implications for the automation of BDI-based agents. Our results suggest that LLMs can enhance agent autonomy by reducing the need for manual plan definition, while maintaining goal-oriented reasoning. Y1 - 2025 U6 - https://doi.org/10.1080/17445760.2025.2541956 SP - 1 EP - 21 PB - Taylor&Francis ER - TY - CHAP A1 - Wagner, Robin A1 - Kitzelmann, Emanuel A1 - Boersch, Ingo T1 - Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review T2 - Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) N2 - Large Language Models (LLMs) demonstrate strong performance on different language tasks, but tend to hallucinate – generate plausible but factually incorrect outputs. Recently, several approaches to integrate Knowledge Graphs (KGs) into LLM inference were published to reduce hallucinations. This paper presents a systematic literature review (SLR) of such approaches. Following established SLR methodology, we identified relevant work by systematically search in different academic online libraries and applying a selection process. Nine publications were chosen for indepth analysis. Our synthesis reveals differences and similarities of how the KG is accessed, traversed, and how the context is finally assembled. KG integration can significantly improve LLM performance on benchmark datasets and additionally to mitigate hallucination enhance reasoning capabilities, explainability, and access to domain-specific knowledge. We also point out current limitations and outline directions for future work. KW - LLMs KW - hallucination KW - knowledge graphs KW - inference KW - literature review Y1 - 2025 UR - https://aclanthology.org/2025.acl-srw.53.pdf U6 - https://doi.org/10.18653/v1/2025.acl-srw.53 SP - 795 EP - 805 PB - Association for Computational Linguistics CY - Vienna ER - TY - THES A1 - Schumacher, Jan Gregor T1 - Untersuchung von Response-Optionen der Wazuh Endpoint-Detection zur Isolierung verdächtiger Endgeräte N2 - Diese Arbeit entwickelt und realisiert automatisierte Reaktionsme-chanismen zur Netzwerkisolierung kompromittierter Endgeräte mit-tels der Open-Source-Plattform Wazuh. Es werden zwei Ansätze als Proof of Concept implementiert und verglichen: eine host-basierte Isolierung durch Deaktivierung der lokalen Netzwerk-schnittstelle (Windows/Linux) und eine netzwerk-basierte Methode, die den Switch-Port des Geräts zentral abschaltet. Die Validierung in einer Testumgebung zeigt, dass die host-basierte Methode eine schnelle Reaktion ermöglicht, während der netzwerk-basierte An-satz unempfindlicher gegenüber Manipulationen auf dem Host ist. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:522-opus4-33624 PB - Technische Hochschule Brandenburg CY - Brandenburg an der Havel ER -