To tackle phishing attacks, recent research works have resorted to the application of
machine learning (ML) algorithms, yielding promising results. Often, a binary classification …

Phishing websites remain a persistent security threat. Thus far, machine learning
approaches appear to have the best potential as defenses. But, there are two main concerns …

Phishing websites are everywhere, and countermeasures based on static blocklists cannot
cope with such a threat. To address this problem, state-of-the-art solutions entail the …

Phishing websites trick users into believing that they are interacting with a legitimate
website, and thereby, capture sensitive information, such as user names, passwords, credit …

Although machine learning based algorithms have been extensively used for detecting
phishing websites, there has been relatively little work on how adversaries may attack such …

Many cyberattacks start with disseminating phishing URLs. When clicking these phishing
URLs, the victim's private information is leaked to the attacker. There have been proposed …

Phishing websites trick honest users into believing that they interact with a legitimate
website and capture sensitive information, such as user names, passwords, credit card …

In recent years, phishing attacks have become more intelligent and more challenging to
detect using typical phishing methods. Moreover, attackers have leveraged some web …

Over the recent decades, numerous evaluations of automated methods for detecting
phishing attacks have been reporting stellar detection performances based on empirical …

Current solutions to tackle phishing employ blocklists that are built from user reports or
automatic approaches. They, however, fall short in detecting zero-day phishing attacks. We …