Paper Info
Title
Hierarchical two-tier ensemble learning: a new paradigm for network intrusion detection
Abstract
Intrusion detection is a mechanism of providing security to computer networks. Almost all of traditional intelligent intrusion detection systems (IDSs) use a single approach to distinguish normal behavior patterns from attack signatures. Moreover these systems have a high false alarm rate and high cost. The combination of multiple classifiers usually exhibits lower false alarm and overall error rate than individual decisions. On the other hand, the combination of classifiers trained on different feature sets could provide better performances than each single classifier. In this paper, a hierarchical two-level combiner is proposed to detect network intrusions using multiple well-known and efficient base classifiers. The proposed combiner exploits the different recognition capabilities provided by the independent feature representations in the first level as well as the agreement among the classifiers in the second level.
Year
DOI
Venue
2007
10.1145/1316874.1316881
PIKM
Keywords
Field
DocType
different feature set,hierarchical two-tier ensemble learning,hierarchical two-level combiner,false alarm,network intrusion,different recognition capability,high cost,high false alarm rate,new paradigm,network intrusion detection,independent feature representation,multiple classifier,intrusion detection,ensemble learning,intrusion detection system,computer network,false alarm rate,error rate
Data mining,False alarm,Computer science,Random subspace method,Anomaly-based intrusion detection system,Artificial intelligence,Classifier (linguistics),Intrusion detection system,Ensemble learning,Pattern recognition,Word error rate,Constant false alarm rate,Machine learning
Conference
Citations 
PageRank 
References 
2
0.40
9
Authors
3
Name
Order
Citations
PageRank
Morteza Analoui112424.94
Behrouz Minaei Bidgoli2282.97
Mohammad Hossein Rezvani3409.06