An Empirical Comparison of Classifiers to Analyze Intrusion Detection

Author

Aggarwal, Preeti ; Sharma, Sudhir Kumar

Author_Institution

Sch. of Eng. & Technol., Ansal Univ., Gurgaon, India

fYear

2015

fDate

21-22 Feb. 2015

Firstpage

446

Lastpage

450

Abstract

The massive data exchange on the web has deeply increased the risk of malicious activities thereby propelling the research in the area of Intrusion Detection System (IDS). This paper aims to first select ten classification algorithms based on their efficiency in terms of speed, capability to handle large dataset and dependency on parameter tuning and then simulates the ten selected existing classifiers on a data mining tool Weka for KDD´99 dataset. The simulation results are evaluated and benchmarked based on the generic evaluation metrics for IDS like F-score and accuracy.

Keywords

Internet; data mining; electronic data interchange; pattern classification; security of data; F-score; IDS; Web; Weka; classification algorithms; data classifiers; data mining tool; generic evaluation metrics; intrusion detection system; malicious activities; massive data exchange; parameter tuning; Accuracy; Classification algorithms; Intrusion detection; Machine learning algorithms; Mathematical model; Measurement; Vegetation; Classification algorithm; Intrusion detection syste; NSL-KDD;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on

Conference_Location

Haryana

Print_ISBN

978-1-4799-8487-9

Type

conf

DOI

10.1109/ACCT.2015.59

Filename

7079125