Kategorie: ‘Theses’
Reasoning-based LLM enhancement for the cybersecurity playbook translation into a standardised format
Automating Incident Response for Power Grids: A Playbook-Based Decision-Making Approach
Privacy-Preserving Data Aggregation for Smart Meters using Temporary Ring-Based Communication Structures
An Evaluation of Similarity-Preserving Bloom Encodings in URL-based Phishing Detection
Leveraging Large Language Models for Enhanced Decision Support in Home Energy Management
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
Applicability of Large Language Models for Evaluating Digital Exercises in Higher Education
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.