Detection of phishing emails using data mining algorithms

Smadi, Sami, Aslam, Nauman, Zhang, Li, Alasem, Rafe and Hossain, Alamgir (2015) Detection of phishing emails using data mining algorithms. In: Proceedings of the 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2015). IEEE, Piscataway, NJ, pp. 1-8. ISBN 9781467367448

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Official URL: http://dx.doi.org/10.1109/SKIMA.2015.7399985

Abstract

This paper proposes an intelligent model for detection of phishing emails which depends on a preprocessing phase that extracts a set of features concerning different email parts. The extracted features are classified using the J48 classification algorithm. We experimented with a total of 23 features that have been used in the literature. Ten-fold cross-validation was applied for training, testing and validation. The primary focus of this paper is to enhance the overall metrics values of email classification by focusing on the preprocessing phase and determine the best algorithm that can be used in this field. The results show the benefits of using our preprocessing phase to extract features from the dataset. The model achieved 98.87% accuracy for the random forest algorithm, which is the highest registered so far for an approved dataset. A comparison of ten different classification algorithms demonstrates their merits and capabilities through a set of experiments.

Item Type: Book Section
Subjects: G400 Computer Science
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 16 May 2016 09:25
Last Modified: 10 Nov 2016 12:38
URI: http://nrl.northumbria.ac.uk/id/eprint/26810

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