Abstract
Nowadays, the internet is growing so rapidly in a lightning way that changes our daily behaviors, from online shopping, online learning to online banking and more activities that make our lives easier. However, using such of ways imposed sharing personal informations such as email, password, credit card information etc. Cybercriminals try to find their victims in the cyberspace by tricking the user using the anonymous structure of the internet. Cybercriminals set out new techniques such as phishing, to deceive victims with the use of false websites, in order to collect their sensitive informations. Understanding whether a web page is legitimate or phishing is a very challenging problem that requires our attention. In this work, we propose a new model that classify whether a web page is legitimate or phishing, based on URLs natural language processing and by applying the n-gram model. We analyze the model with different machine learning algorithms and our system achieves an accuracy of 96.41\(\%\) with 97\(\%\) precision.
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Elkouay, A., Moussa, N., Madani, A. (2022). Classification of URLs Using N-gram Machine Learning Approach. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_7
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