Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System

DOI

10.1080/18756891.2015.1084705How to use a DOI?

Keywords

Intrusion Detection, Data Mining, Machine Learning, Detection accuracy

Abstract

In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Mohanad Albayati
AU  - Biju Issac
PY  - 2015
DA  - 2015/09/01
TI  - Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System
JO  - International Journal of Computational Intelligence Systems
SP  - 841
EP  - 853
VL  - 8
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1084705
DO  - 10.1080/18756891.2015.1084705
ID  - Albayati2015
ER  -