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

Highlights • Exploit patterns in filter measure values to auto-identify an optimal feature subset. • Reduce 79.2% of feature dimensionality without sacrificing detection accuracy. • Boost detection accuracy by using the recommended best performing classifier. • Robust and adaptable to different datasets with significant performance gain. • Prescribe a set of baseline features as the de facto standard for phishing detection. 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. [ABSTRACT FROM AUTHOR]