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10 May 2019 Auto-generating training data for network application classification
Carlos Alcantara, Venkat Dasari, Christopher Mendoza, Michael P. McGarry
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Proceedings Volume 11013, Disruptive Technologies in Information Sciences II; 1101305 (2019) https://doi.org/10.1117/12.2519547
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States
Abstract
We present and evaluate the idea of auto-generating training data for network application classification using a rule-based expert system on two-dimensions of the feature space. That training data is then used to learn classification of network applications using other dimensions of the feature space. The rule-based expert system uses transport layer port number conventions (source port, destination port) from the Internet Assigned Numbers Authority (IANA) to classify applications to create the labeled training data. A classifier can then be trained on other network ow features using this auto-generated training data. We evaluate this approach to network application classification and report our findings. We explore the use of the following classifiers: K-nearest neighbors, decision trees, and random forests. Lastly, our approach uses data solely at the ow-level (in NetFlow v5 records) thereby limiting the volume of data that must be collected and/or stored.
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Carlos Alcantara, Venkat Dasari, Christopher Mendoza, and Michael P. McGarry "Auto-generating training data for network application classification", Proc. SPIE 11013, Disruptive Technologies in Information Sciences II, 1101305 (10 May 2019); https://doi.org/10.1117/12.2519547
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KEYWORDS
Machine learning
Rule based systems
Analytics
Classification systems
Internet
Telecommunications
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