High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Detecting Intrusion in Data Mining using Naive Bayes Algorithm


Shyara Taruna R. , SBCET, Jaipur, India; Mrs. Saroj Hiranwal, SBCET, Jaipur, India


Data Mining, Detection Rate, Falser Positive, Intrusion Detection, Naive Bayes Classifier, Network Security.


With the tremendous increase of network-based services and information sharing on networks, network security is getting more and more importance than ever. Intrusion poses a serious security risk in a network environment. The human classification of the available network audit data instances is usually tedious, time consuming and expensive. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naive Bayes classifier is one of the most popular data mining algorithms for classification, which provides an optimal way to predict the class of an unknown example. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions.

Other Details

Paper ID: IJSRDV1I9017
Published in: Volume : 1, Issue : 9
Publication Date: 01/12/2013
Page(s): 1759-1762

Article Preview

Download Article