- Title
- Performance evaluation of multi-tier ensemble classifiers for phishing websites
- Creator
- Abawajy, Jemal; Beliakov, Gleb; Kelarev, Andrei; Yearwood, John
- Date
- 2012
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/44888
- Identifier
- vital:4887
- Abstract
- This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the toptier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi-tier ensembles for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of multi-level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer.
- Publisher
- Melbourne, Vic School of Information Systems, Deakin University
- Rights
- Copyright 2012 Deakin University
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Phishing websites; Ensemble classifiers; Multi-tier ensembles; Random Forest
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