Intrusion
Detection Technique by using K-means, Fuzzy Neural Network and SVM classifiers.
ABSTRACT:
With the impending era of internet, the network
security has become the key foundation for lot of financial and business web
applications. Intrusion detection is one of the looms to resolve the problem of
network security. Imperfectness of intrusion detection systems (IDS) has given
an opportunity for data mining to make several important contributions to the
field of intrusion detection. In recent years, many researchers are using data
mining techniques for building IDS. Here, we propose a new approach by
utilizing data mining techniques such as neuro-fuzzy and radial basis support
vector machine (SVM) for helping IDS to attain higher detection rate. The
proposed technique has four major steps: primarily, k-means clustering is used
to generate different training subsets. Then, based on the obtained training
subsets, different neuro-fuzzy models are trained. Subsequently, a vector for
SVM classification is formed and in the end, classification using radial SVM is
performed to detect intrusion has happened or not. To illustrate the
applicability and capability of the new approach, the results of experiments on
KDD CUP 1999 dataset is demonstrated. Experimental results shows that our
proposed new approach do better than BPNN, multiclass SVM and other well-known
methods such as decision trees and Columbia model in terms of sensitivity,
specificity and in particular detection accuracy.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System :
Windows XP
ü Programming Language :
JAVA/J2EE.
ü Java Version :
JDK 1.6 & above.
ü Database :
MYSQL
REFERENCE:
Chandrasekhar, A.M, “Intrusion Detection Technique
by using K-means, Fuzzy Neural Network and SVM classifiers”, IEEE Conference on Computer Communication
and Informatics, 2013.