Reinforcement Learning for Attack Intention Inference

This foundational PhD project aims to figure out the intentions of an attacker based on its behavior. Comprehending the motivation of a network attacker is a fundamental aspect of effective cybersecurity. It allows organizations to not only defend against attacks but also to adapt and respond strategically, making the cybersecurity landscape more resilient and secure. Specifically, defenders can tailor their security measures to better protect against specific types of attacks. For example, if the attacker's motivation is financial gain, an organization can focus on bolstering financial security measures and monitoring financial data more closely. The project will utilize cutting-edge reinforcement learning techniques and its training and performance evaluation will be using existing attack simulator tools. In this project we aspire to not only enhance our cybersecurity defenses but also gain invaluable insights into the real motivation of attackers.