This paper is published in Volume-5, Issue-2, 2019
Area
Computer Science And Engineering
Author
Alapati Avinash, Alla Haripriya, B. Sandeep
Org/Univ
Anil Neerukonda Institute of Technology and Sciences, Bheemunipatnam, Andhra Pradesh, India
Pub. Date
02 April, 2019
Paper ID
V5I2-1600
Publisher
Keywords
Intrusion, Anomaly detection, NSL-KDD dataset

Citationsacebook

IEEE
Alapati Avinash, Alla Haripriya, B. Sandeep. Machine learning algorithms for intrusion classification, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Alapati Avinash, Alla Haripriya, B. Sandeep (2019). Machine learning algorithms for intrusion classification. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Alapati Avinash, Alla Haripriya, B. Sandeep. "Machine learning algorithms for intrusion classification." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

The usage of the Internet, the amount of network traffic volume is rapidly raising high. Preventing such huge, sensitive data from security attacks is equally important. Monitoring network traffic may indicate a possible intrusion in the network and therefore anomaly detection is important to detect and prevent such attacks. One of the approaches to anomaly detection is based on machine learning classification techniques. Here we apply seven different machine learning techniques: K-Means, K-Nearest Neighbours (KNN), Fuzzy C-Means (FCM), Support Vector Machine (SVM), Naïve-Bayes (NB), Radial Basis Function (RBF) and Ensemble method (Weighted average method) comprising of K-Nearest Neighbours and Naïve-Bayes (NB) on NSL-KDD dataset and evaluate the performance of these techniques. We also deduced how the change in the training size can affect the training time and the accuracy of each algorithm