Alexander Schwankner M.Sc.

Research Area:

Alexander's research interests are in the area of the application of machine learning in IT security. He is particularly interested in the use of large language models and tries to apply them in the field of cyber threat intelligence. He is also researching anomaly detection in networks, as well as 5G and 6G core network security.

He received his master's degree in IT security and his bachelor's degree in information systems from the Munich University of Applied Science. During his studies he worked as a IT-Security Consultant, Pentester and Software Developer at Protea Networks GmbH for about 4 years. Before his studies, he completed an apprenticeship as an IT specialist for application development at Telemotive AG. Afterwards, he worked as a software developer and team leader for software development as well as team leader for client software services at Telemotive AG for about 8 years.

Seminar/Bachelor/Master Thesis Topics:

Automated attacks and pentests on computer networks with large language models: Recent research shows that large language models such as GPT4 can be used not only for chat but also for generating code and commands. Therefore, this thesis evaluates the use of large language models for the automatic execution of attacks on computer networks. In doing so, the use of code interpreters and command lines is resorted to. To be able to implement this, you need to have knowledge in linux, python and machine learning.

Attack detection in computer networks with large language models: Large language models show their strengths in many areas and have become very capable of solving tasks other than chatting. Therefore, in this thesis you will investigate if and how large language models are able to detect attacks in computer networks.

Evaluation of Machine Learning Approaches for Anomaly Detection in Computer Networks: As the number of attacks on computer networks continues to increase lately, detection and defense must also keep pace. Therefore, this thesis will review and compare current approaches for detecting anomalies and attacks in computer networks.

Creation of a laboratory network representing a small company for the collection of measurement data: In order to obtain data for the research of anomaly detection in networks, a network simulating a small to medium sized enterprise will be built. Not only the server infrastructure is to be created, but also the clients that generate the corresponding traffic. This will be done in a virtualization environment to produce reproducible results.

5G/6G core network lab for anomaly detection: This thesis focuses on the development and implementation of a laboratory for anomaly detection within 5G/6G core networks. It explores innovative techniques for identifying and responding to unusual network activities, ensuring enhanced security and efficiency in next-generation wireless communications. The study combines theoretical knowledge with practical experiments, utilizing advanced algorithms and machine learning to detect and mitigate potential threats in real-time.

Please approach me if you want to propose your own idea within my area of interest.