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Proceedings Paper

Comparison of artificial intelligence classifiers for SIP attack data
Author(s): Jakub Safarik; Jiri Slachta
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Paper Abstract

Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

Paper Details

Date Published: 12 May 2016
PDF: 6 pages
Proc. SPIE 9850, Machine Intelligence and Bio-inspired Computation: Theory and Applications X, 985006 (12 May 2016);
Show Author Affiliations
Jakub Safarik, CESNET z.s.p.o. (Czech Republic)
Jiri Slachta, CESNET z.s.p.o. (Czech Republic)

Published in SPIE Proceedings Vol. 9850:
Machine Intelligence and Bio-inspired Computation: Theory and Applications X
Misty Blowers; Jonathan Williams; Russell D. Hall, Editor(s)

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