Reinforcement Learning for Attack Intention Inference

这个基础博士项目旨在根据攻击者的行为找出其意图。理解网络攻击者的动机是有效网络安全的一个基本方面。它使组织不仅可以防御攻击，还可以进行战略性调整和响应，使网络安全环境更具弹性和安全性。具体而言，防御者可以调整其安全措施，以更好地防范特定类型的攻击。例如，如果攻击者的动机是经济利益，组织可以专注于加强财务安全措施并更密切地监控财务数据。该项目将利用先进的强化学习技术，其培训和性能评估将使用现有的攻击模拟器工具。在这个项目中，我们不仅希望增强我们的网络安全防御，还希望获得对攻击者真实的动机的宝贵见解。

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.