Analyzing the Impact of Search Orders in Extended State Machine Learning for Anomaly Detection
S. van Beek (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S.E. Verwer – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Kubilay Atasu – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Anomaly detection is very important in this day and age. Finding anomalies in
software can help fix bugs or prevent malicious intent from third parties. One way to detect these anomalies is by making use of pdfa’s. Pdfa learning an extension of dfa learning can be used to construct a model of an application given some logs of the application. To build a pdfa, a probabilistic deterministic finite automata, various algorithms in combination with extra parameters can be used. In this paper we will use the FlexFringe framework to look into these so called search orders to answer the question: What are good search orders in extended state machine learning for anomaly detection? We looked into the Alergia and RTI+ algorithm on data with and without time information. Our main conclusions are that Alergia together with the blueblue parameter works best for creating a model for anomaly detection.