Improving Intrusion Detection System Based on KNN and KNN-DS with detection of U2R, R2L attack for Network Probe Attack Detection

Authors(2) :-Prof. Javed Akhtar Khan, Nitesh Jain

This paper describes a hybrid design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named fuzzy belief k-NN classification algorithm. The latter applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The outputs of ensemble feature selecting classifier and data mining classifier are then fused together to get the final decision. The experimental results indicate that hybrid approach effectively generates a more accurate intrusion detection model on detecting both normal usages and malicious activities.

Authors and Affiliations

Prof. Javed Akhtar Khan
Takshshila Institute of Engineering & technology, Jabalpur, Madhya Pradesh, India
Nitesh Jain
Takshshila Institute of Engineering & technology, Jabalpur, Madhya Pradesh, India

Intrusion Detection; Machine Learning; Data Mining

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2016
Date of Publication : 2016-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 209-212
Manuscript Number : IJSRSET162562
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Prof. Javed Akhtar Khan, Nitesh Jain , " Improving Intrusion Detection System Based on KNN and KNN-DS with detection of U2R, R2L attack for Network Probe Attack Detection, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.209-212, September-October-2016.
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