基于改进Stacking策略的钓鱼网站检测研究

doi:10.16088/j.issn.1001-6600.2021071201

Abstract

Abstract: Aiming at the problems of low accuracy of most detection technologies for phishing websites, high consumption of computing resources and untimely detection, a phishing website detection method based on an improved Stacking strategy is proposed. This method integrates multiple base learners with excellent classification performance into a high-performance model through stacking strategy, and takes the input characteristics and prediction results of the first level of the stacking algorithm as the input characteristics of the second level at the same time, so as to give full play to the advantages of high precision and fast speed of each model, and further improve the performance of the model. Experimental results show that, compared with traditional machine learning phishing website detection technology, this integrated learning algorithm on a 100,000-level data set shows better performance on multiple indicators, with accuracy rate of 97.82% and F₁ value reach 97.54%, which can effectively detect phishing websites.

Key words: phishing website, base learner, Stacking algorithm, feature extraction, ensemble learning

CLC Number:

TP393.08

HU Qiang, LIU Qian, ZHOU Hangxia. Study on Phishing Website Detection Based on Improved Stacking Strategy[J].Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 132-140.

References

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Study on Phishing Website Detection Based on Improved Stacking Strategy

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