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Cited by 348

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

Open access
Chiew, Kang Leng, Tan, Choon Lin, Wong, KokSheik, Yong, Kelvin S.C., Tiong, Wei King·2019·Information Sciences
DOI 10.1016/j.ins.2019.01.064
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Abstract

This paper proposes a new feature selection framework for machine learning-based phishing detection system, called the Hybrid Ensemble Feature Selection (HEFS). In the first phase of HEFS, a novel Cumulative Distribution Function gradient (CDF-g) algorithm is exploited to produce primary feature subsets, which are then fed into a data perturbation ensemble to yield secondary feature subsets. The second phase derives a set of baseline features from the secondary feature subsets by using a function perturbation ensemble. The overall experimental results suggest that HEFS performs best when it is integrated with Random Forest classifier , where the baseline features correctly distinguish 94.6% of phishing and legitimate websites using only 20.8% of the original features. In another experiment, the baseline features (10 in total) utilised on Random Forest outperforms the set of all features (48 in total) used on SVM, Naive Bayes, C4.5, JRip, and PART classifiers. HEFS also shows promising results when benchmarked using another well-known phishing dataset from the University of California Irvine (UCI) repository. Hence, the HEFS is a highly desirable and practical feature selection technique for machine learning-based phishing detection systems.

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Metadata

Authors
Chiew, K. L., Tan, C. L., Wong, K., Yong, K. S. C., & Tiong, W. K.
Venue
Information Sciences
Publisher
Elsevier BV
Published
2019-05
Volume
484
Pages
153-166
ISSN
0020-0255 (ISSN-L)
DOI
10.1016/j.ins.2019.01.064
OpenAlex
W2914801588
Cited by
348
References
17
FWCI
49.67
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. https://app.cochat.ai/papers/10.1016/j.ins.2019.01.064
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Chiew, K. L., Tan, C. L., Wong, K., Yong, K. S. C., & Tiong, W. K. (2019). A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Information Sciences. https://doi.org/10.1016/j.ins.2019.01.064

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