Honeypots are the digital traps used by cybersecurity professionals to lure in attackers. These traps imitate real systems and services, such as web servers or IoT devices, to appear as genuine targets. The goal of a honeypot is to deceive attackers into interacting with them, enabling security experts to observe and analyze their behavior.
Challenge and Proposed Solution
Traditional honeypots have limitations as they can be easily identified by skilled attackers and struggle to adapt to new threats. By applying RL, we can develop a smart honeypot deployment system that learns and adapts to emerging threats in real time. Imagine a cybersecurity team using a Reinforcement Learning-driven honeypot to combat phishing attacks. The honeypot monitors incoming emails and adjusts its responses based on the perceived threat level of each email. Over time, it learns to identify phishing attempts more effectively and assists the team in understanding how attackers alter their tactics.
