Markus Wurzenberger

Scientist in the Cyber Security research group at the AIT Austrian Institute of Technology in Vienna, Austria.


AIT Austrian Institute of Technology

Giefinggasse 4

1210 Vienna, Austria

Markus Wurzenberger is a scientist and project manager at AIT Austrian Institute of Technology, located in Vienna, Austria. Since 2014 he is part of the cyber security research group of AIT’s Center for Digital Safety and Security. His main research interests are log data analysis with focus on anomaly detection and cyber threat intelligence (CTI). Besides the involvement in several international and national research projects, Markus is one of the key researchers working on AIT’s anomaly detection project AECID. Among the most prominent solutions developed within this project, Markus and his team created AMiner, a software component for log analysis, which implements several anomaly detection algorithms that base on machine learning (ML) artificial intelligence (AI), and statistics. The AMiner is available as package in the official Debian distribution.

In 2021, Markus finished his PhD in computer science at TU Wien. His thesis focused on resource-efficient log analysis to enable online anomaly detection in cyber security. In 2015 Markus obtained his master’s degree in technical mathematics at the TU Wien. Since 2014 he is a full-time scientist at AIT in the area of cyber security.


May 30, 2022 Launch of new website.

selected publications

  1. TOPS
    Dealing with Security Alert Flooding: Using Machine Learning for Domain-Independent Alert Aggregation
    Landauer, Max, Skopik, Florian,  Wurzenberger, Markus, and Rauber, Andreas
    ACM Trans. Priv. Secur. Apr 2022
  2. TREL
    Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed
    Landauer, Max, Skopik, Florian,  Wurzenberger, Markus, Hotwagner, Wolfgang, and Rauber, Andreas
    IEEE Transactions on Reliability Mar 2021
  3. C&S
    System log clustering approaches for cyber security applications: A survey
    Landauer, Max, Skopik, Florian,  Wurzenberger, Markus, and Rauber, Andreas
    Computers & Security May 2020
  4. C&S
    Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection
    Landauer, Max,  Wurzenberger, Markus, Skopik, Florian, Settanni, Giuseppe, and Filzmoser, Peter
    Computers & Security Nov 2018
  5. ARES’17
    Incremental Clustering for Semi-Supervised Anomaly Detection Applied on Log Data
    Wurzenberger, Markus, Skopik, Florian, Landauer, Max, Greitbauer, Philipp, Fiedler, Roman, and Kastner, Wolfgang
    In Proceedings of the 12th International Conference on Availability, Reliability and Security Aug 2017