Paper Info
Title
Comparative performance analysis of classification algorithms for intrusion detection system.
Abstract
The ability of an intrusion detection system (IDS) to accurately detect potential attacks is crucial in protecting network resources and data from the attack's destructive effects. Among many techniques available for incorporation into IDS to improve its accuracy, classification algorithms have been demonstrated to produce impressive and efficient results in detecting IPv4-based attacks but have not yet been investigated in IPv6-based attacks. This paper aims to present the result of a comparative analysis on the performance of three classifier algorithms, namely, decision tree, random forest, and k-nearest neighbor (k-NN), to detect an IPv6-based attack, specifically ICMPv6-based DoS flooding. The experimental results showed that there is no single best algorithm that outperforms others in all measured metrics. k-NN has the lowest false-positive outcome while RF has the lowest false-negative (missed attacks) percentage.
Year
Venue
Keywords
2016
2016 14TH ANNUAL CONFERENCE ON PRIVACY, SECURITY AND TRUST (PST)
Decision tree,Intrusion detection system,k-nearest neighbor,Random forest,IPv6-based attack
Field
DocType
ISSN
Decision tree,Data mining,IPv4,Computer science,Computer security,Robust random early detection,Artificial intelligence,Classifier (linguistics),Random forest,Intrusion detection system,IPv6,Statistical classification,Machine learning
Conference
1712-364X
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Mohammed Anbar1169.05
Rosni Abdullah215624.82
Iznan H. Hasbullah300.68
Yung-Wey Chong400.68
Omar E. Elejla511.37