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|Title:||CatchPhish: detection of phishing websites by inspecting URLs|
|Citation:||Journal of Ambient Intelligence and Humanized Computing, 2020, Vol.11, 2, pp.813-825|
|Abstract:||There exists many anti-phishing techniques which use source code-based features and third party services to detect the phishing sites. These techniques have some limitations and one of them is that they fail to handle drive-by-downloads. They also use third-party services for the detection of phishing URLs which delay the classification process. Hence, in this paper, we propose a light-weight application, CatchPhish which predicts the URL legitimacy without visiting the website. The proposed technique uses hostname, full URL, Term Frequency-Inverse Document Frequency (TF-IDF) features and phish-hinted words from the suspicious URL for the classification using the Random forest classifier. The proposed model with only TF-IDF features on our dataset achieved an accuracy of 93.25%. Experiment with TF-IDF and hand-crafted features achieved a significant accuracy of 94.26% on our dataset and an accuracy of 98.25%, 97.49% on benchmark datasets which is much better than the existing baseline models. 2019, Springer-Verlag GmbH Germany, part of Springer Nature.|
|Appears in Collections:||1. Journal Articles|
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