Automatic detection of phishing pages with event-based request processing, deep-hybrid feature extraction and light gradient boosted machine model

Ömer Kasim ()
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Ömer Kasim: Kutahya Dumlupinar University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2021, vol. 78, issue 1, No 9, 103-115

Abstract: Abstract Cyber attackers target unconscious users with phishing methods is a serious threat to cyber security. It is important to quickly detect benign web pages according to legitimate ones. Despite the successful detection of phishing in the studies suggested in the literature, the problems of high false positive rate after the web page request is processed should be resolved. The novelty of the study is that classification of deep-hybrid features with the Light Gradient Boosted Machine model is evaluated as an event when the web address is entered on the address bar of the browser. Thus, phishing can be detected at every request entry before the process is completed. In the proposed approach, normalized features from requests of web pages are applied to Sparse Autoencoder and Principal Component Analysis methods. These methods contribute to encoding of the deep-hybrid feature extraction. Light Gradient Boosted Machine model classifier can effectively distinguish legitimate pages and phishing attacks using these features. The ISCX-URL phishing dataset is used to measure performance of the proposed approach and validate it. The proposed method classifies the features that are encoded with SAE-PCA by using the Light Gradient Boosted Machine model at the rate of 99.6% within the event. The obtained results show that the proposed approach performs better classification performance metrics than most others. This accuracy contributed to the solution of the false-positives problem before requests are processed compared to other models.

Keywords: Event-based request processing; Phishing detection; Sparse autoencoder; Principal component analysis; Light gradient boosted machine model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11235-021-00799-6

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