Reinforcement Learning Model for Detecting Phishing Websites

The growing appearance of cyber dangers, particularly phishing attempts, poses a severe challenge to internet security. This research work describes a unique framework for detecting phishing that uses Reinforcement Learning’s (RL) Q-Learning algorithm. The suggested methodology takes advantage of RL's capacity to study ideal policies through contact with the circumstances, allowing the model to dynamically adapt to evolving phishing trends. The study adopts a state-action-reward paradigm, in which states represent features retrieved from the URL, actions reflect judgements on whether the URL is legitimate or malicious, and incentives are established depending on classification accuracy. Extensive studies on different and large-scale phishing datasets indicate the usefulness of the Q-Learning-based RL technique in outperforming standard machine learning (ML) models in detection performance. This study's findings show the potential of RL-based techniques, notably Q-Learning, in improving phishing detection capabilities. This research adds to creating proactive and flexible cybersecurity measures, ultimately bolstering defenses against the ever-changing landscape of phishing attempts by harnessing the power of reinforcement learning. This paper establishes the framework for future research efforts in applying RL approaches for cybersecurity, emphasizing the promise of dynamic learning paradigms in addressing complex and dynamic threats in the digital world.