Abstract
Today, phishing is one of the most serious cyber-security threat in which attackers steal sensitive information such as personal identification number (PIN), credit card details, login, password, etc., from Internet users. In this paper, we proposed a machine learning based anti-phishing system (i.e., named as PHISH-SAFE) based on Uniform Resource Locator (URL) features. To evaluate the performance of our proposed system, we have taken 14 features from URL to detect a website as a phishing or non-phishing. The proposed system is trained using more than 33,000 phishing and legitimate URLs with SVM and Naïve Bayes classifiers. Our experiment results show more than 90% accuracy in detecting phishing websites using SVM classifier.
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Jain, A.K., Gupta, B.B. (2018). PHISH-SAFE: URL Features-Based Phishing Detection System Using Machine Learning. In: Bokhari, M., Agrawal, N., Saini, D. (eds) Cyber Security. Advances in Intelligent Systems and Computing, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-8536-9_44
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DOI: https://doi.org/10.1007/978-981-10-8536-9_44
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