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Proceedings Paper

Neural network classifier of attacks in IP telephony
Author(s): Jakub Safarik; Miroslav Voznak; Miralem Mehic; Pavol Partila; Martin Mikulec
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Paper Abstract

Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

Paper Details

Date Published: 22 May 2014
PDF: 7 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 91180X (22 May 2014); doi: 10.1117/12.2050671
Show Author Affiliations
Jakub Safarik, VŠB-Technical Univ. of Ostrava (Czech Republic)
Miroslav Voznak, VŠB-Technical Univ. of Ostrava (Czech Republic)
Miralem Mehic, VŠB-Technical Univ. of Ostrava (Czech Republic)
Pavol Partila, VŠB-Technical Univ. of Ostrava (Czech Republic)
Martin Mikulec, VŠB-Technical Univ. of Ostrava (Czech Republic)


Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)

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