piwik-script

Intern
    Data Science Chair

    Security and Fraud

    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.

    Projects

    We are currently working on the following projects:

    DeepScan

     

     Application and development of methods of machine learning and artificial intelligence for the
     automated detection of security incidents and manipulation attempts in enterprise software.

     

     

    Publications

    Here is a list of selected publications.

    • Flow-based network traffi... - Download
      Flow-based network traffic generation using Generative Adversarial Networks. Ring, Markus; Schlör, Daniel; Landes, Dieter; Hotho, Andreas in Computers & Security (2019). 82 156 - 172.
       
    • IP2Vec: Learning Similari... - Download
      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.
       
    • Creation of Flow-Based Da... - Download
      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 a... - Download
      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, Kalutarage, H. K., Huang, Y. (eds.) (2017). 3--31.