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

By K. Chiew, Choon Lin Tan, Koksheik Wong, Kelvin S. C. Yong, W. Tiong. Information Sciences, 2019

332 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/fb4791e31425f4f02c1435f85ce9f24c3f2a8d4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chaitanya Kulkarni
2. Mayur Wankhade
3. A. C. Rao

In Information Systems

1. Chaitanya Kulkarni
2. Mayur Wankhade
3. A. C. Rao

Citations in the corpus

• A comprehensive survey of AI-enabled phishing attacks detection techniques (2020, methodology): “[19] used two datasets one from 5000 phishing web-pages based on URLs from PhishTank and second OpenPhish.”
• A survey on sentiment analysis methods, applications, and challenges (2022, background): “Numerous hybrid feature selection algorithms for sentiment analysis have been developed (Chiew et al. 2019).”