Automaton Models. Short Overview DRAFT. C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
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1 Automaton Models for Netflow Analysis Fingerprinting and Classifying Participants NMRG Workshop, Prague, Czech Republic Friday, July 24th 2015 Christian A Hammerschmidt,christian.hammerschmidt@uni.lu Interdisciplinary Centre for Security, Reliability and Trust University of Luxembourg
2 Automaton Models Short Overview C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
3 Fingerprinting with Automatons Prediction, Classification, and Visualization (I) Prediction I predicting next states I detecting outliers and anomalies unsupervised Classification I classifying flows I identifying type of activity or infection (semi-) supervised C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
4 Fingerprinting with Automatons Prediction, Classification, and Visualization (I) Prediction I predicting next states I detecting outliers and anomalies unsupervised Classification I classifying flows I identifying type of activity or infection (semi-) supervised C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
5 Fingerprinting with Automatons Prediction, Classification, and Visualization (II) animation of automaton C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
6 Challenges NetFlow Data as a (Regular) Language guide/ip6-netflow_v9.fm/_jcr_content/renditions/ip6-netflow_v9-1.jpg C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
7 Challenges NetFlow Data as a (Regular) Language From regression of numeric values to classification: I via clustering to obtain few representatives or through discretization I via binning to obtain a discrete state space What to choose? C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
8 Method Learning State Structure from Data 2 2 Taken from [2] C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
9 Evaluation Data Set Experiments (on time-aggregated flow data): 1. predicting statistics for next flows 2. classifying flows on unlabeled data 3. classifying flows on labeled data 3 3 Using a botnet traffic data set[1] C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
10 Evaluation Generated Automatons C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
11 Evaluation Excerpt Data Set Experiment Error / F 1 / FPR C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
12 Conclusion Conclusion and Future Work Results I structure learning on netflow data is feasible I initial results look very promising I this is still work-in-progress and offers a number of ways to improve Further Research I compare performance to other fingerprinting solutions I apply a more expressive automaton model C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
13 Conclusion Conclusion and Future Work Results I structure learning on netflow data is feasible I initial results look very promising I this is still work-in-progress and offers a number of ways to improve Further Research I compare performance to other fingerprinting solutions I apply a more expressive automaton model C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
14 Future Work and Extensions Currently Ongoing Research 4 4 Taken from [2] C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
15 Thank You! Time for questions. C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
16 References I García, S. and Grill, M. and Stiborek, J. and Zunino, A. An empirical comparison of botnet detection methods Computers & Security, S. E. Verwer, C. Witteveen, M. M. De Weerdt. Efficient identification of timed automata: Theory and practice, March Heule, M.J.H., Verwer, S., Software model synthesis using satisfiability solvers. Empirical Software Engineering 18, , 2013 C. Hammerschmidt (SnT) Automaton Models for NetFlows SnT / 13
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