# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 1 Introduction  
Phishing has established itself as one of the most pervasive and damaging forms of cyberattacks, responsible for a large percentage of data breaches, financial theft, and personal information compromises. Phishing attacks typically involve deceptive tactics such as fraudulent emails, websites, or messages designed to lure victims into revealing sensitive information like usernames, passwords, or financial data. The frequency and complexity of phishing attacks have escalated in recent years, challenging organizations to adopt more robust defense mechanisms. According to a 2023 report from the Anti-Phishing Working Group (APWG), phishing attacks rose by nearly 40% compared to the previous year, marking it as one of the most significant threats in the cybersecurity landscape [1]. Traditional methods of phishing detection, such as RSA-based encryption methods [2], have been largely ineffective in addressing this evolving threat landscape. These approaches include blacklist-based detection, which flags known phishing URLs, and rule-based systems that rely on predefined patterns and heuristics. However, these solutions fall short when it comes to detecting novel and sophisticated phishing attacks that evade static defenses [3]. For example, attackers have developed advanced spear-phishing techniques, where emails are carefully crafted to target specific individuals or organizations, making it much harder to detect using conventional methods [4]. Furthermore, the emergence of phishing-as-a-service (PaaS), in which cybercriminals sell ready-made phishing kits to others, has significantly lowered the barrier to entry,enabling even those with little technical expertise to launch phishing attacks [5]. Recent research has shown that ML offers a promising avenue for enhancing phishing detection. Unlike traditional rule-based systems, ML models learn from historical data, enabling them to identify patterns in phishing attacks [6]. Supervised learning algorithms, such as Support Vector Machines (SVMs), decision trees, and neural networks, have demonstrated success in phishing detection by analyzing specific features of phishing emails and websites. These models have been effective in certain scenarios but often require large amounts of labeled data to function optimally and struggle to adapt to new or unseen phishing tactics [7; 8]. Moreover, machine learning (ML) models can generate high false positive rates, where legitimate emails or websites are incorrectly flagged as phishing attempts, leading to reduced trust in these systems [9]. Recent advancements in DL and RL aim to address these challenges by improving adaptability and reducing false positives in phishing detection systems [10]. This research focuses on leveraging RL as an advanced approach to optimize phishing detection systems. RL, a subset of artificial intelligence, offers a dynamic learning framework that allows systems to adapt over time based on feedback from their environment. Unlike supervised learning, which depends on labeled datasets, RL agents learn through trial and error by receiving rewards for correct decisions and penalties for mistakes. This makes RL particularly well-suited for dynamic environments like cybersecurity, where new threats are constantly emerging [11]. The idea of applying RL to phishing detection is relatively new, but promising studies have demonstrated its potential to improve both the accuracy of phishing detection systems and their ability to generalize new attack patterns. A study by [12] explored the use of Q-learning in network intrusion detection systems and found that RL-based models were able to adapt more effectively than traditional ML models in identifying anomalous behavior in network traffic [13; 14]. The objective of this research was to develop an AI-driven threat-hunting system for phishing detection that is optimized using RL. Our model aims to address several key challenges: increasing detection accuracy, reducing the rate of false positives, and adapting to emerging phishing threats.