Intrusion detection system by improved preprocessing methods and Naïve Bayes classifier using NSL-KDD 99 Dataset

Author

Deshmukh, Datta H. ; Ghorpade, Tushar ; Padiya, Puja

Author_Institution

Dept. Of Comput. Eng., Ramrao Adik Inst. Of Technol., Navimumbai, India

fYear

2014

fDate

13-14 Feb. 2014

Firstpage

1

Lastpage

7

Abstract

Today Network is one of the very important parts of life and a lot of essential activities are performed using network. Network security plays critical role in real life situations. This paper presents a Data Mining method in which various preprocessing methods are involved such as Normalization, Discretization and Feature selection. With the help of these methods the data is preprocessed and required features are selected. Here Naïve Bayes classifier is used in supervised learning method which classifies various network events for the KDD cup´99 Dataset. This dataset is the most commonly used dataset for Intrusion Detection.

Keywords

computer network security; data mining; feature selection; learning (artificial intelligence); pattern classification; KDD cup´99 dataset; NSL-KDD 99 dataset; data mining method; discretization; feature selection; improved data preprocessing methods; intrusion detection system; naïve Bayes classifier; network security; normalization; supervised learning method; Data mining; Niobium; Probes; Training; Correlation Based Feature Selection; Cross validation; Discretization; Knowledge Discovery in Databases; Naive Bayes; Normalization;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics and Communication Systems (ICECS), 2014 International Conference on

Conference_Location

Coimbatore

Print_ISBN

978-1-4799-2321-2

Type

conf

DOI

10.1109/ECS.2014.6892542

Filename

6892542