Performance analysis of NSL-KDD dataset using ANN

Author

Ingre, Bhupendra ; Yadav, Anamika

Author_Institution

Dept. of Electr. Eng., Nat. Inst. of Technol., Raipur, India

fYear

2015

fDate

2-3 Jan. 2015

Firstpage

92

Lastpage

96

Abstract

Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated using ANN. The result obtained for both binary class as well as five class classification (type of attack). Results are analyzed based on various performance measures and better accuracy was found. The detection rate obtained is 81.2% and 79.9% for intrusion detection and attack type classification task respectively for NSL-KDD dataset. The performance of the proposed scheme has been compared with existing scheme and higher detection rate is achieved in both binary class as well as five class classification problems.

Keywords

Internet; mobile computing; neural nets; pattern classification; security of data; ANN; Internet; NSL-KDD dataset; anomalous traffic detection; attack type classification; class classification; intrusion detection system; security; smart devices; Accuracy; Artificial neural networks; Biological neural networks; Intrusion detection; Probes; Testing; Training; ANN; Intrusion Detection System; NSL-KDD dataset; accuracy;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing And Communication Engineering Systems (SPACES), 2015 International Conference on

Conference_Location

Guntur

Type

conf

DOI

10.1109/SPACES.2015.7058223

Filename

7058223