An Intrusion Detection System for Identifying Attacks using Classification Technique
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 4
Abstract
Information security is one of the important role to protect the information from unauthorized person. Intrusion detection is a classifier to classify the data as normal and various types of attacks. Data mining based decision tree algorithm play very important role to develop the robust IDS to classify the attacks which is harmful for our system. In this research work, used decision tree techniques as classifier to classify the attacks. We have also develop the robust ensemble model which is combination of C4.5, Simple CART and decision tree that gives better accuracy. Our proposed ensemble model gives 99.70% with 80-20% training –testing partition. We have also applied the feature selection technique to computationally increase the performance of model. Our proposed model gives 99.80% in 11 features with info gain feature selection technique while 98.80% in 16 features with gain ratio feature selection technique.
Authors and Affiliations
Aanchal Tiwari, Rohit Miri, Amit Kumar Dewangan
Keywords
Related Articles
- EP ID EP23976
- DOI http://doi.org/10.22214/ijraset.2017.4270
- Views 286
- Downloads 9
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