Share Email Print
cover

Proceedings Paper • new

Auto-generating training data for network application classification
Author(s): Carlos Alcantara; Venkat Dasari; Christopher Mendoza; Michael P. McGarry
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 10 May 2019
PDF: 7 pages
Proc. SPIE 11013, Disruptive Technologies in Information Sciences II, 1101305 (10 May 2019); doi: 10.1117/12.2519547
Show Author Affiliations
Carlos Alcantara, The Univ. of Texas at El Paso (United States)
Venkat Dasari, U.S. Army Research Lab. (United States)
Christopher Mendoza, The Univ. of Texas at El Paso (United States)
Michael P. McGarry, The Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 11013:
Disruptive Technologies in Information Sciences II
Misty Blowers; Russell D. Hall; Venkateswara R. Dasari, Editor(s)

© SPIE. Terms of Use
Back to Top