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Multilayer hybrid strategy for phishing email zero-day filtering
journal contribution
posted on 2017-12-01, 00:00 authored by Morshed ChowdhuryMorshed Chowdhury, Jemal AbawajyJemal Abawajy, Andrei Kelarev, T HochinThe cyber security threats from phishing emails have been growing buoyed by the capacity of their distributors to fine-tune their trickery and defeat previously known filtering techniques. The detection of novel phishing emails that had not appeared previously, also known as zero-day phishing emails, remains a particular challenge. This paper proposes a multilayer hybrid strategy (MHS) for zero-day filtering of phishing emails that appear during a separate time span by using training data collected previously during another time span. This strategy creates a large ensemble of classifiers and then applies a novel method for pruning the ensemble. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization-based pruning. This paper introduces and investigates a multilayer hybrid pruning. Its application in MHS combines all three approaches in one scheme: ranking, clustering, and optimization. Furthermore, we carry out thorough empirical study of the performance of the MHS for the filtering of phishing emails. Our empirical study compares the performance of MHS strategy with other machine learning classifiers. The results of our empirical study demonstrate that MHS achieved the best outcomes and multilayer hybrid pruning performed better than other pruning techniques.
History
Journal
Concurrency and computation: practice and experienceVolume
29Issue
23Season
Special IssueArticle number
e3929Pagination
1 - 12Publisher
WileyLocation
Chichester, Eng.Publisher DOI
ISSN
1532-0626eISSN
1532-0634Language
engNotes
Special Issue: Combined Special issues on Applications and techniques in information and network security (CSTA2015) and International conference on innovative network systems and applications held under the federated conference on computer science and information systems (FedCSis‐INetSApp2015)Publication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2016, WileyUsage metrics
Categories
Keywords
attribute selectionensemblesfilteringmultilayer strategyphishing emailspruningScience & TechnologyTechnologyComputer Science, Software EngineeringComputer Science, Theory & MethodsComputer SciencePRUNING ALGORITHMENSEMBLECLASSIFICATIONArtificial Intelligence and Image ProcessingComputer SoftwareDistributed Computing
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