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攻击者的意图。了解网络攻击者的动机是有效的网络安全的一个基本方面。它使组织不仅可以抵御攻击，还可以战略性地适应和响应，使网络安全环境更具弹性和安全性。具体来说，防御者可以调整他们的安全措施，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，那么组织可以将重点放在加强财务安全措施和更密切地监视财务数据上。该项目将利用尖端的强化学习技术，其训练和性能评估将使用现有的攻击模拟器工具。在这个项目中，我们不仅希望加强我们的网络安全防御，还希望获得对攻击者真实动机的宝贵见解。

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.