# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 2 Background and Related Work
However, while RL has been successfully applied to areas such as network intrusion detection and malware detection, its application to phishing detection is still relatively unexplored; [PERSON] et al. [21] proposed an RL-based framework for network intrusion detection that demonstrated superior performance in detecting anomalous network traffic compared to traditional ML models. Their framework relied on DQN architecture, which allowed the system to learn from raw network traffic data and make more informed decisions. This research highlights the potential of RL to enhance cybersecurity solutions, but its focus on network traffic leaves a gap in the literature regarding phishing-specific detection systems. In phishing detection, RL's ability to adapt to new and unseen phishing attacks offers a significant advantage over static ML models; [PERSON] et al. [22] explored the use of deep reinforcement learning (DRL) for phishing detection, emphasizing the importance of an adaptive approach in the ever-changing threat landscape. Their research showed that DRL models could achieve higher detection accuracy and lower false positive rates compared to traditional ML approaches. However, their study focused on the theoretical application of DRL, and there remains a need for practical implementation and evaluation in real-world phishing detection systems. Figure 1 below illustrates the fundamental RL framework used in this study, depicting the continuous interaction cycle between the agent and the environment. In this RL model, the agent evaluates states (\(S_{t}\)) and performs actions (\(A_{t}\)) based on its learned policy. The environment then responds by providing a reward (\(R_{t+1}\)) and transitioning to a new state (\(S_{t+1}\)), allowing the agent to iteratively refine its decision-making process through dynamic feedback. Another critical challenge in phishing detection is the high rate of false positives. Many ML models, including those based on supervised learning, tend to overfit specific patterns in the training data, leading to an increase in false positives when applied to new data; [PERSON] et al. [23] addressed this issue by proposing a hybrid ML model that combined multiple classifiers to reduce false positives in phishing detection. Although their model  
Figure 1: Reinforcement learning agent adapts phishing detection through continuous environment interaction. showed improved performance, it still required extensive manual tuning and lacked the ability to dynamically adapt to evolving phishing techniques. The proposed work differs from existing research in several ways. First, we focused on developing a practical, AI-driven phishing detection framework that is optimized using RL. Unlike previous studies that have primarily explored ML approaches or theoretical RL models, our research aimed to implement a fully functional RL-based phishing detection system that can be deployed in real-world cybersecurity environments. Second, our work emphasizes the adaptive nature of RL, which allows the system to continuously improve its detection capabilities as new phishing techniques emerge. This stands in contrast to traditional ML models, which require retraining to adapt to new threats. Additionally, we proposed an approach to reduce false positives in phishing detection by leveraging the reward and penalty structure of RL. In our framework, the RL agent is penalized for generating false positives, which incentivizes the model to improve its decision-making process and achieve a more accurate classification of phishing attempts. This approach addresses one of the key limitations of existing phishing detection systems and provides a more effective solution for real-world applications. Furthermore, our research provides a detailed comparison of the RL-based phishing detection model with state-of-the-art ML techniques. While prior studies have demonstrated the potential of RL in cybersecurity, there is limited research on its comparative effectiveness in phishing detection.