Volume & Issue no: Volume 5, Issue 3, May - June 2016
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Title: |
INTRUSION DETECTION IN DYNAMIC DISTRIBUTED NETWORK USING MACHINE LEARNING BASED ALGORITHMS |
Author Name: |
Niraj S.Patil, Chitrakant Banchhor |
Abstract: |
Abstract: Intrusion detection is a significant focus of
research in the security of computer networks. This paper
presents an analysis of the progress being made in the
development of effective intrusion detection. Network security
becomes more complex due to the changing environment of
network and new type of attacks. So it is necessary to design
dynamic system to detect new type of attacks. In this paper we
define the solution to frequently changing network
environment and new types of attacks. The designed system
contains two models, Local model and Global model. In the
local model, online Gaussian mixture models (GMMs) and
online Adaboost processes are used as weak classifiers. A
global detection model is constructed by combining the local
parametric model. This combination is achieved by using an
algorithm based on particle swarm optimization (PSO) and
support vector machines (SVM). This system is able to detect
new types of attacks. It gives high detection rate and low false
alarm rate.
Keywords: Adaboost; detection rate; false alarm rate;
network intrusions; parameterized model. |
Cite this article: |
Niraj S.Patil, Chitrakant Banchhor , "
INTRUSION DETECTION IN DYNAMIC DISTRIBUTED NETWORK USING MACHINE LEARNING BASED ALGORITHMS" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 5, Issue 3, May - June 2016 , pp.
194-199 , ISSN 2278-6856.
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