Kategorie: ‘Theses’

Automating Incident Response for Power Grids: A Playbook-Based Decision-Making Approach

June 7th, 2024 | by

Privacy-Preserving Data Aggregation for Smart Meters using Temporary Ring-Based Communication Structures

June 6th, 2024 | by

An Evaluation of Similarity-Preserving Bloom Encodings in URL-based Phishing Detection

May 28th, 2024 | by

LLM-based Tool for FAIR Data Assessment

May 17th, 2024 | by

A Condensation-based Anonymization Approach for Intrusion Detection

May 8th, 2024 | by

Local Differential Privacy Preserving Intrusion Detection Systems

May 8th, 2024 | by

Leveraging Large Language Models for Enhanced Decision Support in Home Energy Management

April 25th, 2024 | by

Large language models (LLMs) have proven the ability to assist diverse users in conducting a variety of individual tasks via intuitive and natural conversations. This thesis discusses a utilization of LLMs as a tool for informed decision-making in energy investments and operations. One major goal is to transform the way the consumers engage with home energy management systems and reduce expertise-related dependencies.

Generating security playbooks from attack-defense trees using Large Language Models

April 22nd, 2024 | by

Applicability of Large Language Models for Evaluating Digital Exercises in Higher Education

April 22nd, 2024 | by

As universities strive to enhance the effectiveness of their lecture exercises, there arises a need for diverse and realistic test user scenarios to evaluate the understandability and usefulness of educational materials. However, in creating such scenarios a number of challenges arise: Real world students can rarely be used for testing, they are likely inexperienced or experienced but have little incentive to refresh their knowledge to test tasks for lectures. In addition, leaking tasks to students can lead to skewed learning results during the actual course. On the other hand, simple unit tests can neglect the importance of a task being understandable to a human and do not offer much insight into task difficulty.

Knowledge Graph Construction from Biomedical Literature using Large Language Models

April 17th, 2024 | by

Fine-tuning pre-trained large language models (LLMs) enhances biomedical text mining. This thesis introduces a tool capable of performing tasks such as Named Entity Recognition (NER), Normalization (NEN), and Knowledge Graph Construction (KGC). A key research question explores how LLMs can address the challenges of named entity recognition, normalization, and relation extraction in biomedical contexts.