Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features
PositiveArtificial Intelligence
- A new study has introduced a Quantile Regression Deep Q-Network (QR-DQN) approach that enhances phishing detection by integrating RoBERTa semantic embeddings with traditional lexical features. This method aims to improve the accuracy and stability of detecting phishing attempts, achieving a test accuracy of 99.86% on a dataset of 105,000 URLs from various sources including PhishTank and OpenPhish.
- The QR-DQN framework represents a significant advancement in cybersecurity, particularly in combating phishing attacks that deceive individuals into revealing sensitive information. By leveraging deep reinforcement learning techniques, this approach addresses the growing sophistication of phishing schemes, potentially reducing financial losses for victims.
- The development of QR-DQN aligns with ongoing efforts in the AI community to enhance detection systems for various online threats, including misinformation and visual phishing attempts. As cyber threats evolve, integrating advanced machine learning techniques, such as perceptual hashing and parameter-efficient fine-tuning methods, is becoming increasingly crucial for maintaining robust security measures.
— via World Pulse Now AI Editorial System
