Abstract
Phishing is an illegitimate method to collect secret information of any person or organization. Information like debit card, credit card details, PIN no, OTP, passwords, etc. are stolen by the attackers through phishing sites. Researchers have used different techniques to detect those phishing sites. But it is difficult to stay on a particular technique as attackers come with new tactics. In this paper, phishing and legitimate URL classifications are performed based on the lexical features of URLs. Feature selection technique is used to select the relevant features only. Accuracy for all combination of features with different numbers of features each time was evaluated to find the best possible combination of features. Performances are analyzed for different datasets with various parameters using four different machine learning techniques.
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Banik, B., Sarma, A. (2020). Lexical Feature Based Feature Selection and Phishing URL Classification Using Machine Learning Techniques. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_9
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