DocumentCode :
1688245
Title :
Highlights on analyzing one-way traffic using different tools
Author :
Balkanli, Eray ; Zincir-Heywood, A. Nur
Author_Institution :
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
fYear :
2015
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, we present our analysis using four different systems on two different one-way network traffic data sets. Specifically, we have explored the usage of two network traffic analyzers, namely Corsaro and Cisco ASA 5515-X, and two machine learning based systems, namely the C4.5 Decision Tree classifier and the AdaBoost.M1 classifier. We have employed these four systems on two publicly available one-way network data sets provided by CAIDA from 2008 and 2012. Our analysis on these systems are based on the detection rate, false alarm rate, computational cost and ease of use of these systems. To the best of our knowledge, this work is the first one performing such an analysis and evaluating machine learning based systems against well known commercial as well as open source ones on one-way network traffic data sets.
Keywords :
computer network security; decision trees; learning (artificial intelligence); telecommunication traffic; AdaBoost.M1 classifier; C4.5 decision tree classifier; CAIDA; Cisco ASA 5515-X; different tools; false alarm rate; machine learning; network traffic data sets; one-way network traffic data sets; one-way traffic analysis; Backscatter; Decision trees; IP networks; Monitoring; Protocols; Security; Training; One-way traffic; machine learning; network traffic monitoring;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Security and Defense Applications (CISDA), 2015 IEEE Symposium on
Conference_Location :
Verona, NY
Print_ISBN :
978-1-4673-7556-6
Type :
conf
DOI :
10.1109/CISDA.2015.7208635
Filename :
7208635
Link To Document :
بازگشت