Title :
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
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;
Conference_Titel :
Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on
Conference_Location :
Haryana
Print_ISBN :
978-1-4799-8487-9
DOI :
10.1109/ACCT.2015.59
