Anomaly Intrusion Detection Using An Hybrid Of Decision Tree And K-Nearest Neighbor

No Thumbnail Available

Files

JASRA-V2N1P7.pdf (411.46 KB)

Date

2015-03-30

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Journal of Advances in Scientific Research & Applications (JASRA), Faculty of Science, Adeleke University, Ede, Osun state.

Abstract

With the dictate of the 21st century making the economy to be information-driven, securing this valuable asset becomes interesting engagement. Intrusion detection plays a vital role in this regard by allowing prospective attacks or threats to information resources by unauthorized person(s) be detected and prevented. Previous researchers have identified the need for a robust approach to intrusion detection. This paper presents an overview of intrusion detection and a hybrid classification algorithm based on decision tree and K Nearest neighbour. The data set is first passed through the decision tree and node information is generated. Node information is determined according to the rules generated by the decision tree. This node information (as an additional attribute) along with the original set of attributes is passed through the KNN to obtain the final output. The key idea here is to investigate whether the node information provided by the decision tree will improve the performance of the KNN. A performance evaluation is performed using a 10-fold cross validation technique on the individual base classifiers (decision tree and KNN) and the proposed hybrid classifier (DT-KNN) using the KDD Cup 1999 dataset on WEKA tool. Experimental results show that the hybrid classifier (DT-KNN) gives the best result in terms of accuracy and efficiency compared with the individual base classifiers (decision tree and KNN).

Description

Keywords

Intrusion Detection, Machine Learning, Data Mining, Network Security

Citation

Balogun A. O. and Jimoh R. G., "Anomaly Intrusion Detection Using an Hybrid Of Decision Tree And K-Nearest Neighbor", A Multidisciplinary Journal Publication of the Faculty of Science, Adeleke University, Ede, Nigeria, 2015.

URI

https://www.researchgate.net/profile/Abdullateef_Balogun/publication/282326950_Anomaly_Intrusion_Detection_Using_an_Hybrid_Of_Decision_Tree_And_K-Nearest_Neighbor/links/560bfb7d08ae73e7a6a2d26a/Anomaly-Intrusion-Detection-Using-an-Hybrid-Of-Decision-Tree-And-K-Nearest-Neighbor.pdf
 http://hdl.handle.net/123456789/257

Collections