TY - CHAP A1 - Graf, Julian A1 - Hachani, Murad A1 - Fischer, Sebastian A1 - Hackenberg, Rudolf T1 - A heuristic packet processing model for improved encrypted network analysis T2 - CSCS '25: Proceedings of the 2nd Cyber Security in CarS Workshop N2 - Modern networked systems, such as those in the automotive sector, face increasing complexity and growing attack surfaces due to the rise of interconnected and data-driven technologies. Detecting malicious behavior in these environments requires efficient and scalable methods that can operate reliably despite limited resources and high communication volumes. This paper proposes a heuristic packet processing model designed to support intrusion detection based on structural and temporal characteristics of encrypted network traffic. The model follows a modular architecture consisting of four key phases: recording, sorting, prioritizing, and analyzing. At the core of the approach is the Polymetric Queueing Topology Space, a feature space that combines statistical and time series attributes derived from model structure and flow data. These features serve as input for machine learning models, which can effectively distinguish between benign and intrusion traffic patterns without relying on packet data beyond the transport layer. The approach was evaluated using the publicly available ToN_IoT dataset and demonstrated that reliable classification is achievable using a subset of the developed feature space that contains model-derived traffic features. We used Random Forest for supervised binary and multi-class classification achieving high accuracy scores of 99% for binary and 98% for multi-class classification. Additionally, for unsupervised anomaly detection, we created an Isolation Forest model accomplishing F1-scores of 0.92 for the benign and 0.96 for intrusion class. The architecture is designed to enable dynamic traffic prioritization and to offer a flexible foundation that can observe diverse network domains while maintaining efficient performance under constrained computational conditions. Y1 - 2025 U6 - https://doi.org/10.1145/3736130.3764510 PB - ACM CY - New York, USA ER - TY - CHAP A1 - Dünnweber, Jan A1 - Lorenz, Marcel A1 - Bräu, Fabian A1 - Saito, Hiroshi A1 - Melzer, Matthias T1 - Driving out the Bears from the Suburbs by Means of Location Theory and Intelligent Agents T2 - Proceedings of the ACM 12th International Conference on Animal-Computer Interaction, ACI 2025: The International Conference on Animal-Computer Interaction ; Indiana USA ; 01-04.12.2025 N2 - Due to the recent surge in bear attacks, affected municipalities have taken various measures to drive out the intruders. Political considerations include, in many places, the expansion of shooting permits. More and more regional governmental authorities invest in technologies for efficient bear hunting, such as IP cameras and computer vision. From an ecological perspective, such procedures, which are all solely aimed at decreasing the bear population, are questionable, since ecosystems can benefit from a peaceful coexistence of bears with humans: Bears remove animal carcasses and thus return vital nutrients to the soil and prevent the spread of diseases. Moreover, the bears keep the deer population in check, which is advantageous for the forest vegetation. Conflicts start, once the bears intrude populated areas, which are often rural villages and suburbs with a low population density. Naturally, bears do not see humans as prey and respect their size. When the animal attacks, this happens rather for territorial or protective reasons, e. g., after an unexpected encounter with humans unsettled it. In this work, we analyze means to eradicate the danger: We dynamically apply Weiszfeld’s algorithm for finding the shifting geometric median between time-windowed black bear sightings and use the results for implementing an early warning system. In addition, we weigh the spotted locations and predict the expected migration behavior between bear habitats using a simulation with intelligent agents. As a case study, we apply our software to data from Romania and Fukushima and identify the positions where bear repelling facilities should be placed most effectively. Y1 - 2025 SN - 9798400721236 U6 - https://doi.org/10.1145/3768539.3768546 PB - ACM CY - New York, NY, USA ER - TY - JOUR A1 - Schmidbauer, Lukas A1 - Lobe, Elisabeth A1 - Schaefer, Ina A1 - Mauerer, Wolfgang T1 - It’s Quick to be Square: Fast Quadratisation for Quantum Toolchains JF - ACM Transactions on Quantum Computing N2 - Many of the envisioned use-cases for quantum computers involve optimisation processes. While there are many algorithmic primitives to perform the required calculations, all eventually lead to quantum gates operating on quantum bits, with an order as determined by the structure of the objective function and the properties of target hardware. When the structure of the problem representation is not aligned with structure and boundary conditions of the executing hardware, various overheads degrading the computation may arise, possibly negating any possible quantum advantage. Therefore, automatic transformations of problem representations play an important role in quantum computing when descriptions (semi-)targeted at humans must be cast into forms that can be “executed” on quantum computers. Mathematically equivalent formulations are known to result in substantially different non-functional properties depending on hardware, algorithm and detail properties of the problem. Given the current state of noisy intermediate-scale quantum (NISQ) hardware, these effects are considerably more pronounced than in classical computing. Likewise, efficiency of the transformation itself is relevant because possible quantum advantage may easily be eradicated by the overhead of transforming between representations. In this paper, we consider a specific class of higher-level representations, that is, PUBOs, and devise novel automatic transformation mechanisms into widely used QUBOs that substantially improve efficiency and versatility over the state of the art. In addition, we conduct a comprehensive investigation of industry-relevant problem formulations and their conversion into a quantum-specific representation, identifying significant obstacles in scaling behaviour and demonstrating how these can be circumvented. KW - Algorithmic optimisation KW - Graphs KW - Performance KW - Pseudo Boolean Function Y1 - 2026 U6 - https://doi.org/10.1145/3800943 N1 - Preprint ebenfalls auf OPUS veröffentlicht: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7937 VL - 7 IS - 2 PB - ACM ER -