z-logo
open-access-imgOpen Access
Intrusion Detection System Using Support Vector Machine and Decision Tree
Author(s) -
Snehal A. Mulay,
Prakash Devale,
G.V. Garje
Publication year - 2010
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/758-993
Subject(s) - computer science , decision tree , intrusion detection system , support vector machine , data mining , intrusion , tree (set theory) , machine learning , artificial intelligence , geology , mathematical analysis , mathematics , geochemistry
Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the training and testing time, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying an algorithm, Tree structured multiclass SVM, which has been used for classifying data. This paper proposes the decision tree based algorithm to construct multiclass intrusion detection system.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom