EurekAlert!

Smarter machine-learning models improve phishing website detection

Febraury 10, 2026—Phishing websites remain a persistent cybersecurity threat, exploiting users by imitating trusted online services. New machine-learning tools could help organisations flag more ...
Nature

PhishNet 1.0: optuna-optimized stacking ensemble with Boruta-based feature selection for phishing URL detection

The objective of this research is to enhance phishing detection through ensemble learning integrated with well-structured metaheuristic algorithms. Various classifiers, including Logistic Regression, ...
Scientific Research Publishing

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features ()

Phishing is a form of cybercrime in which people are deceived into exposing their personal information which can result in ...
Nature

Cacography based Ransomware Email Phishing Attack Prevention using Language Pack Tuned Transformer Language Model

The proposed cacography-based architecture tuned transformer method prevents novel ransomware email phishing (REP) attacks. Cybercriminals now create ransomware email phishing attacks through ...
Securities.io

Crypto Security Needs Market-Aware Phishing Detection

In the modern age where most assets and valuable information are digitalized, phishing scams are an important way criminals steal money, much higher than “normal” physical robbery & extortion, with up ...