Graduate School of Informatics

As next-generation avionic platforms become increasingly connected, systems that were once isolated are now exposed to cyber threats. Communication standards like MIL-STD-1553, widely used in commercial, military, and aerospace platforms, were originally designed without security considerations, resulting in growing vulnerabilities. Implementing conventional security upgrades is costly and brings certification challenges. Intrusion Detection Systems (IDS) offer a non-intrusive alternative, requiring no hardware or software changes. This study aims to enhance the security of MIL-STD-1553 communication buses by integrating a hardware fingerprinting-based IDS and evaluates the effectiveness of machine and deep learning methods in detecting unauthorized devices on the bus.

Date: 02.06.2025 / 11:00 Place: A-212

This thesis aims to reduce the need for pixel-level labeled data for semantic segmentation. A DeepLabV3+ based model trained on synthetic images is supplemented with a Domain-Adversarial Neural Network (DANN), an adversarial domain adaptation method, to adapt to real images. The model is applied in unsupervised and semi-supervised domain adaptation scenarios. In the semi-supervised adaptation method in particular, similar performance was achieved using 92% less labeled real data compared to the DeepLabV3+ method trained with supervised learning and without domain adaptation. This study provides an effective solution that reduces the burden of image labeling.

Date: 26.05.2025 / 15:00 Place: A-212

A common evolutionary origin of the Obscurin and Trio protein families was identified through phylogenetic analyses, indicating ancient domain shuffling events across vertebrate lineages. Building on these findings, the novel variant effect predictor TrioNsight was developed for TrioN-like DH domains. The predictor, which integrates structural, evolutionary and physicochemical features, achieved high accuracy in distinguishing pathogenic variants (MCC: 0.906; 1.0 indicates perfect prediction). This work bridges fundamental evolutionary biology and clinical application, offering both mechanistic insights into protein family evolution and a practical tool for the interpretation of disease-associated variants in functionally critical domains.

Date: 30.05.2025 / 10:00 Place: A-212

In this thesis, a forecasting model has been developed for cooling and freezing products of a home appliance company. Economic fluctuations, global events, and market competition increase demand variability, complicating supply chain management. In this context, various clustering techniques have been utilized to improve product and country groupings, aiming to enhance forecasting accuracy and optimize supply chain strategies. Additionally, external factors such as the impact of Covid-19, economic indicators, and stock levels have been incorporated into the forecasting model.

Date: 26.05.2025 / 10:30 Place: B-116

Flavor perception and gut-brain signaling influence eating behavior. The vagus nerve (VN) may become desensitized to high-calorie foods, promoting overeating, a process potentially reversible through VN stimulation. This study examined whether transcutaneous VNS (tVNS) modulates brain responses to food using EEG during chocolate milkshake consumption. Key findings: (1) Spontaneous eyeblink rates (a dopamine proxy) increased after food intake, suggesting dopaminergic engagement. (2) Event-related potentials (ERPs) time-locked to swallowing were only observed with food stimuli, validating the sip-and-swallow protocol for flavor research. (3) No significant tVNS effects were found on resting-state oscillations or ERPs, but the method captured food-specific neural responses. These results support the protocol’s utility for future gut-brain studies.

Date: 14.04.2025 / 14:00 Place: A-212