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Phishing attacks, which have exponentially increased in recent years, are a form of cyber attack aiming to steal sensitive credentials of innocent users. In general, the attackers attempt to deceive users by creating and submitting a fake but visually similar version of a legitimate web page, which has already been in usage. In this study, we suggest an approach for recognition of phishing web pages by utilizing two global image descriptors namely GIST and local binary patterns (LBP) which have never been employed in phishing web page recognition literature. Moreover, in order to obtain a discriminative representation, we have experimented two kinds of visual feature extraction scheme such as (1) “holistic” and (2) “multi-level patches”. While we have only used whole web page screenshot in “holistic” scheme, screenshots were divided into equally sized smaller crops at growing number of levels during the implementation of “multi-level” patches scheme. In order to evaluate the proposed approach, we have employed a publicly available phishing web page dataset in literature including screenshots of both 14 different highly phished brands and legitimate web pages posing an open-set problem for researchers. Besides, the aforementioned dataset covers 1313 training and 1539 testing cases in total. The visual signatures extracted by use of GIST and LBP descriptors were then fed to various machine learning models such as SVM, Random Forest and XGBoost (regularized gradient tree boosting). According to the results of comprehensively conducted experiments, XGBoost has been found as the best learner. In line with this finding, we obtained 87.7% (GIST) and 83.1% (LBP) validation accuracy along with the representation of “multi-level patches”. Consequently, it has been shown that preferred global image descriptors can be successfully employed for detecting and recognizing phishing web pages. In addition, average required time for processing one screenshot (around 1.12 sec.) with GIST descriptors indicates that the proposed scheme and GIST can be effectively used as a browser based plug-in for recognizing brands of phishing web pages.

Brand Recognition of Phishing Web Pages via Global Image Descriptors