Manuscript Number : IJSRSET162562
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.
Prof. Javed Akhtar Khan
Intrusion Detection; Machine Learning; Data Mining
Publication Details
Published in :
Volume 2 | Issue 5 | September-October 2016 Article Preview
Takshshila Institute of Engineering & technology, Jabalpur, Madhya Pradesh, India
Nitesh Jain
Takshshila Institute of Engineering & technology, Jabalpur, Madhya Pradesh, India
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
Journal URL :
https://ijsrset.com/IJSRSET162562