ÆCID: A self-learning Anomaly Detection Approach Based on Light-weight Log Analytics
2019-11-30, 14:05–14:45 (Europe/Vienna), Dachsaal

Existing signature-based intrusion detection systems are based on manually-defined patterns that are known to correspond to particular attacks and are therefore unable to disclose any previously unknown threats, such as zero day exploits. ÆCID (Automatic Event Correlation for Incident Detection) alleviates this problem by employing self-learning anomaly detection. ÆCID is capable of automatically learning the complex syntax of log files, classify events, and extract relevant parameters for advanced analysis. This includes the derivation of rules regarding the correlation of events as well as occurrences of parameter values. In addition, ÆCID carries out statistical analyses on the observed values and reports all significant changes of system behavior to security analysts. ÆCID’s open-source log sensor, the AMiner that enables efficient log parsing, allows to build log analysis pipelines using a number of modules. The AMiner is designed as a light-weight component that fits seamlessly into any system and has minimal requirements regarding processing power and required memory. Finally, the AMiner in combination with ÆCID supports connection to existing security solutions, such as SIEMs, by providing interfaces to standard message queue technologies, such as Kafka.

Our talk will consist of two parts: First, we will discuss some basic considerations when it comes to log data analysis and outline our strategies of tackling the encompassed challenges, including the parsing of logs from heterogeneous sources and design of anomaly detection methods. Then, we will present some selected features of ÆCID in a practical demonstration.

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Max Landauer, MSc, finished his Bachelor’s Degree in Business Informatics at the Vienna University of Technology in 2016. In 2017, he joined the Austrian Institute of Technology in 2017 where he carried out his Master Thesis. He started his PhD studies in 2018 and is currently employed as a Junior Scientist at AIT. His main research interests are anomaly detection, cyber threat intelligence, and log data analysis.

Markus Wurzenberger, MSc, finished his Bachelor’s Degree in Mathematics in Science and Technology in 2013. In 2014 he joined AIT as a freelancer and finished his Master’s Degree in Technical Mathematics in 2015. In the end of 2015 he joined AIT’s Cyber Security research group as Junior Scientist and is working on national and international research projects. His primary research interest is log data analysis with focus on anomaly detection. In 2016 Markus started his PhD studies in Computer Science.