# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 2 Background and Related Work  
Phishing attacks have become one of the most common and damaging cyber threats faced by individuals and organizations worldwide. These attacks exploit social engineering tactics to deceive users into divulging sensitive information, such as passwords, financial data, and personal details. With the rapid growth of online communication, the sophistication of phishing attacks has also evolved, making it increasingly difficult for traditional detection mechanisms to keep pace. As phishing tactics continue to become more complex, cybersecurity researchers and professionals are exploring new technologies, such as AI and ML, to enhance phishing detection capabilities. In the past decade, several studies have examined various methods for detecting phishing attacks, particularly focusing on machine learning-based solutions. These approaches rely on the analysis of specific features extracted from phishing emails, websites, or URLs. For instance, [PERSON] et al. [17] explored the use of decision trees and support vector machines (SVMs) to classify phishing websites based on specific URL characteristics, such as domain age, HTTPS presence, and URL length. More recently, [PERSON] et al. [6] reviewed deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and found that these models can significantly enhance phishing detection rates by analyzing email content. These models not only improve detection accuracy but also address the issue of false positives, which has been a major challenge in traditional ML models. While these models have shown promise, they are often constrained by the availability of labeled data and their inability to generalize to new, unseen phishing tactics. A key limitation of traditional ML approaches in phishing detection lies in their dependence on static datasets. These models are typically trained in historical data and are unable to adapt effectively to emerging phishing strategies. For example, recent studies have explored ML approaches to phishing detection, such as SVMs and decision trees, but they still face challenges in generalizing new types of phishing attacks, especially those that use novel obfuscation techniques like URL shortening services, domain shadowing, and homograph attacks [18, 19]. Recent advancements in AI have led to the development of RL techniques, which offer a promising solution to the challenges faced by traditional ML models in phishing detection. RL allows systems to learn dynamically from their environment by receiving feedback in the form of rewards or penalties. RL achieves superior adaptability compared to traditional ML models due to its autonomous interaction with dynamic environments through Markov Decision Processes (MDPs). Unlike supervised learning, where models are trained on fixed labeled datasets, an RL agent continuously observes the current state, performs an action, and receives real-time feedback in the form of rewards. Through iterative cycles of exploration and exploitation, the agent optimizes its policy by learning from new environmental conditions, without requiring re-training on newly labeled data. The cumulative reward structure ensuresthat the agent gradually adjusts its decision-making to evolving phishing techniques, improving its detection capability even as attack patterns change. This intrinsic feedback loop makes RL particularly suitable for cybersecurity domains characterized by uncertainty and variability. This adaptive learning process enables RL-based models to continuously enhance their detection capabilities, making them particularly effective in cybersecurity environments where threats are constantly evolving, as demonstrated by [PERSON] et al. [20] the potential of RL in real-time phishing detection by training a Q-learning model to identify phishing emails based on their content and metadata. The study showed that RL models could outperform traditional ML models in terms of accuracy and adaptability.