Phishing is a fraudulent tactic for attackers to obtain victims personal information, such as
passwords, account details, credit card details, and other sensitive information. Existing anti …

Malicious actors often use phishing attacks to compromise legitimate users' credentials.
Machine learning is a promising approach for phishing detection. While the accuracy of …

Supervised machine learning is often used to detect phishing websites. However, the
scarcity of phishing data for training purposes limits the classifier's performance. Further …

Internet-based economies and societies are drowning in deceptive attacks. These attacks
take many forms, such as fake news, phishing, and job scams, which we call" domains of …

Despite two decades of research on automatic filtering systems, phishing attacks remain a
serious problem. To alleviate risks from filtering failures, we design and evaluate the …

The use of encryption for network communication leads to a significant challenge in
identifying malicious traffic. The existing malicious traffic detection techniques fail to identify …

Identification and analysis of latent and emergent behavioral patterns are core tasks in
investigative domains such as homeland security, counterterrorism, and crime prevention …

We describe version 2.0 of our benchmarking framework, PhishBench. With the addition of
the ability to dynamically load features, metrics, and classifiers, our new and improved …

Cyber attacks such as phishing, IRS scams, etc., still are successful in fooling Internet users.
Users are the last line of defense against these attacks since attackers seem to always find a …

One emerging research focus is cyber threat intelligence and analytics, which seeks to
integrate and deploy different computing techniques such as big data analytics, sentiment …