The application and development of machine learning methods in the field of (network) security and fraud is an active field of research in the Data Science Chair. In the DeepScan project, we are developing methods to detect anomalies, ICT security incidents and fraudulent behaviour in business software. Other research projects are currently working on the detection of security incidents in corporate networks or on application layer.
We are currently working on the following projects:
Here is a list of selected publications.
IP2Vec: Learning Similarities Between IP Addresses. Ring, Markus; Landes, Dieter; Dallmann, Alexander; Hotho, Andreas in 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017). 657–666.
Flow-based network traffic generation using Generative Adversarial Networks. Ring, Markus; Schlör, Daniel; Landes, Dieter; Hotho, Andreas in Computers & Security (2019). 82 156–172.
Creation of Flow-Based Data Sets for Intrusion Detection. Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas in Journal of Information Warfare (2017). 16(4) 41–54.
A Toolset for Intrusion and Insider Threat Detection. Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas in Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications, I. Palomares Carrascosa, H. K. Kalutarage, Y. Huang (eds.) (2017). 3–31.