P352 - INFORMATIK 2024 - Lock in or log out? Wie digitale Souveränität gelingt
Auflistung P352 - INFORMATIK 2024 - Lock in or log out? Wie digitale Souveränität gelingt nach Erscheinungsdatum
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- KonferenzbeitragUnderstanding stegomalware in ICS: Attacks and Prevention(INFORMATIK 2024, 2024) Edeh, Natasha; Yatagha, Romarick; Mejri, Oumayma; Waedt, KarlThis research investigates the growing threat of stego-malware in Industrial Control Systems (ICS), where attackers utilize steganography to embed malicious code covertly. Such attacks pose significant challenges due to their ability to evade traditional detection methods. The study reviews current cybersecurity frameworks and detection techniques, highlighting their strengths and limitations against stego-malware. It explores various detection approaches, including signature-based, anomaly-based, and AI/ML-based methods, assessing their effectiveness within the context of ISO/IEC 27001 and IEC 62443 standards. Case studies such as Havex and Industroyer underscore the real-world impact of stego-malware on ICS infrastructure. The research advocates for enhanced integration of AI and machine learning to bolster steganalysis capabilities, and proposes improvements to existing cybersecurity frameworks to address steganographic threats more effectively. By bridging gaps in current knowledge, this study contributes to advancing cybersecurity measures tailored to protect critical ICS environments against evolving cyber threats.
- KonferenzbeitragMachine Learning in Glass Bottle Printing Quality Control: A Collaboration with a Medium-Sized Industrial Partner(INFORMATIK 2024, 2024) Bundscherer, Maximilian; Schmitt, Thomas H.; Bocklet, TobiasIn cooperation with a medium-sized industrial partner, we developed and evaluated two ML-based approaches for quality control in glass bottle printing. Our first approach utilized various filters to suppress reflections, image quality metrics for image comparison, and supervised classification models, resulting in an accuracy of 84%. We used the ORB algorithm for image alignment and to estimate print rotations, which may indicate manufacturing anomalies. In our second approach, we fine-tuned pre-trained CNN models, which resulted in an accuracy of 87%. Utilizing Grad-CAM, an Explainable AI method, we localized and visualized frequently defective bottle print regions without explicitly training our models for this use case. These insights can be used to optimize the actual manufacturing process beyond classification. This paper also describes our general approach and the challenges we encountered in practice with data collection during ongoing production, unsupervised preselection, and labeling.
- KonferenzbeitragPhotogrammetric Analysis for Accurate Three-Dimensional Reconstruction of the Mudhafaria Minaret in Erbil (Kurdistan Region of Iraq)(INFORMATIK 2024, 2024) Mustafa, Sima; Qader, AlandThis paper presents a photogrammetry-based approach for the digital reconstruction of the Mudhafaria Minaret in Erbil, Kurdistan Region of Iraq. Hundreds of two-dimensional images were transformed into a detailed three-dimensional model of the minaret using photogrammetric methods and software. The photographs were captured from various perspectives, making sure that all angles of the minaret were documented. The data were then uploaded onto specialized software that generated a detailed 3D model. The employment of three-dimensional scanning makes it possible to conserve cultural assets and accurately chronicle historical events with greater ease. Through the use of digital reconstruction, we are able to acquire a more in-depth comprehension of our shared history, as well as a deeper respect for it. In order to achieve this goal, it’s necessary for us to demonstrate that photogrammetry is not only dependable but also flexible for use in the field of cultural heritage.
- KonferenzbeitragTrustworthy Data Exchange: Leveraging Linked Data for Enhanced IDS Certification(INFORMATIK 2024, 2024) Hackel, Sascha; Makohl, Marie-Elisabeth; Petrac, SimonIn today’s data-driven business environments, ensuring secure and controlled data sharing is essential. However, existing solutions often lack mechanisms to maintain data sovereignty and establish trust among ecosystem participants. This paper presents a novel approach to the establishment of International Data Spaces (IDS) certification, focusing on the use of a formal information model and Linked Data. The proposed certification framework leverages the formal information model to define the structure and semantics of the certification process. It enables machine-recognizable data representations, ensuring interoperability and facilitating automated processing of certification information. Using Linked Data principles, the framework provides an exact description of system capabilities and requirements. This increases transparency and facilitates more reliable and trustworthy data exchange within IDS.
- KonferenzbeitragForensic strategies and methods in advanced software-defined networks(INFORMATIK 2024, 2024) Weijers, Florian; Jensen, Meiko; Raab-Düsterhöft, AntjeWhen it comes to network forensics in modern cloud-edge-systems, network forensics has become an urgent yet challenging field of work. Especially forensics of software-defined networks (SDN) poses some unique challenges that need to be addressed. This article hence addresses the methodological and strategic challenges of network forensics in modern complex software-defined networks using the ZeroTier Network as a practical example. In this context, detailed strategies and methods for clarification and preservation of evidence in SDN after common IT security incidents are derived from existing best practices in digital forensics. In addition, typical technical and legal issues and obstacles for forensic work in SDN are addressed in connection with IT security measures, and possible solution approaches are presented. Using an advanced SDN example, characteristic workflows of network forensics in SDN are discussed. The result of the work is ultimately a presentation of adapted and individually adaptable strategies and methods for applying targeted digital forensics in advanced SDN.
- KonferenzbeitragDer forensische Wert von Thumbnails und Cache-Dateien im Ermittlungsverfahren(INFORMATIK 2024, 2024) Herchel, Sabrina; Steinert, Mirijam; Dehne, Vivien; Labudde, DirkIn der IT-Forensik nehmen Thumbnails und Cache-Dateien als Beweismittel eine wichtige Rolle ein. Dies liegt insbesondere an ihrer Eigenschaft, automatisch generiert und temporär gespeichert zu werden. Dieser Artikel betrachtet die Verwendbarkeit dieser Datentypen in gerichtlichen Verfahren. Dabei werden die technischen Hintergründe sowie die forensischen Techniken zur Sicherung und Auswertung von Beweisen erläutert. Es ist essenziell für Ermittler, eine klare Unterscheidung zwischen Thumbnails und Cache-Dateien treffen zu können, da beide oft verschiedene Auskünfte über die Herkunft und Nutzung der Daten geben. Thumbnails sind in der Regel verkleinerte Ansichten von Bildern, während Cache-Dateien häufig Kopien von Webseiten oder anderen Medien beinhalten, die zur Beschleunigung des Zugriffs abgelegt werden. Diese Differenzierung gewinnt insbesondere bei Fragen zur Herkunft und zum Kontext der Daten an Bedeutung. Das Ziel ist es, die Bedeutung von Thumbnails und Cache-Dateien in der täglichen Fallarbeit zu erläutern, ihre Unterscheidbarkeit darzustellen und ihren Beitrag zur Beweisführung hervorzuheben.
- KonferenzbeitragOhne Big Brother und Cloud, aber nicht ohne Probleme: Fallbeispiele zu Implikationen smarter Technik mit einfachen Sensoren im Zuhause(INFORMATIK 2024, 2024) Börner, Andy; Köpferl, Karola; Lehmann, Tanja; Becker, Alexa; Berger, Arne; Bischof, Andreas; Kurze, AlbrechtMit der Verbreitung von IoT-Geräten im Zuhause, mit einer Vielzahl von Sensoren, die Daten sammeln und speichern, entstehen Probleme, insbesondere für Privatheit. Diese lassen sich vermeintlich leicht lösen, wenn keine Produkte von großen Tech-Konzernen genutzt werden, um eine Speicherung und Analyse von Daten in der Cloud oder durch einen „Big Brother“ zu vermeiden. Wir zeigen anhand von drei Fallbeispielen aus der Literatur, dass es selbst bei einfachen Sensordaten hintergründig doch nicht so einfach ist. Abschließend beschreiben wir unseren Forschungsansatz „Privacy by Co-Design“ dazu.
- KonferenzbeitragHollerithEnergyML: A Prototype of a Machine Learning Energy Consumption Recommender System(INFORMATIK 2024, 2024) Zanger, Michael; Schulz, Alexander; Grodmeier, Lukas; Agaj, Dion; Schindler, Rafael; Weiss, Lukas; Möhring, MichaelEnergy consumption aspects of machine learning classifiers are important for research and practice as well. Due to sparse research in this area, a prototype of a recommender system was developed to provide energy consumption recommendations of different possible classifiers. The prototype is demonstrated as well as discussed and future research points are derived.
- KonferenzbeitragReal-time collection and archiving of related posts to cultural heritage on the cloud(INFORMATIK 2024, 2024) Rashid, Shaimaa; Qasha, RawaaX platform (previously Twitter) produces massive amounts of data related to all aspects of our daily life. Such data can be utilized for archiving interesting information that can be extracted from this data. Since the field of cultural heritage is one of the crucial aspects that a huge number of posts focus on, the daily updates obtained from X have made it possible to record all related posts to the cultural heritage in a comprehensive and real-time way. This data can contribute to the important process of archiving and digitizing the cultural heritage of the ancient community, which is a significant part of its conservation and preservation. This paper is designed to implement a container-based cloud framework for real-time collection and archiving of posts related to the cultural heritage of Nineveh. The framework is designed to manage various types of X data, including images, text data, and video. All collected data is taken from the social media server to the cloud storage.
- KonferenzbeitragA multi-label text classifier approach for understanding electronic word-of-mouth of restaurants on Google Maps(INFORMATIK 2024, 2024) Hering, FrederikRestaurant owners need to understand and react to customer feedback to remain competitive in the long term. Customers provide essential feedback electronically via online platforms such as Google Maps. To better understand customer feedback, we developed a multi-label text classifier to classify feedback into categories of aspects customers criticize and comment on. Since restaurants, like many small and medium-sized enterprises, do not have the resources to maintain computationally intensive deep learning architectures, we present a simple knowledge distillation approach in this paper. On the test dataset, our approach performs better than a BERT model at a much smaller model size and with significantly better inference time. These results provide a novel approach to understanding electronic word of mouth for small and medium-sized enterprises.