The past decade has witnessed the rapid development of natural language processing and
machine learning in the phishing detection domain. However, there needs to be more …

There exist numerous solutions to detect malicious URLs based on Natural Language
Processing and machine learning technologies. However, there is a lack of comparative …

The unique nature of website URLs has made phishing detection a challenging task. Unlike
natural language, URLs have an unstructured nature with non-linear and sophisticated …

Abstract [eng] Phishing detection is mostly performed through the usage of blacklists.
However, blacklists cannot be exhaustive and lack the ability to detect newly generated …

There has been ever-growing use of Internet and progress within many communication
channels such as social media and this escalates the need for rapid and low source …

Deep learning models for phishing URL classification based on character-and word-level
URL features achieve the best performance in terms of accuracy. Various improvements …

Machine Learning (ML) and Deep Learning (DL) methods have become indispensable in
cybersecurity. Recently, they are often used to detect and classify phishing websites …

Browsers often include security features to detect phishing web pages. In the past, some
browsers evaluated an unknown URL for inclusion in a list of known phishing pages …

In recent years, cyber threats including malicious software, virus, spam, and phishing have
grown aggressively via compromised Uniform Resource Locators (URLs). However, the …

Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a
form of fraud that attracts people and businesses to access malicious uniform resource …