CT-ISG: Learning to Protect Computer Networks via Proactive Vulnerability Assessment

该项目将人工智能（AI）技术应用于计算机网络中的主动漏洞评估（VA）。目前基于库的VA方法不允许利用库外的已知攻击特征的漏洞。该提案通过开发先进的人工智能技术来学习攻击计算机网络，从而在这些弱点被利用之前发现其漏洞和弱点，从而朝着下一代主动VA软件迈出了第一步。最初的工作将VA置于强化学习（RL）的框架内，因为这种方法已经证明了其他网络问题的成功。RL研究人员研究了基于与环境交互时收到的奖励信号为一个或多个代理学习高奖励策略的算法。对于VA，环境对应于特定的计算机网络;奖励信号为对网络有害的活动提供正奖励，并为被检测为恶意的活动提供负奖励（因此不会损害网络）。RL发现的策略为一个或多个代理提供了一种方法，可以在不被检测到的情况下攻击网络。在这个提议中，重点是使用RL技术来发现对等网络中的VA。该项目的更广泛影响将包括弥合人工智能和网络研究社区之间的差距，以提供强大的网络攻击防御。研究结果将通过网站http://www.eecs.orst.edu/~thinhq/research/AI_Security/index.html传播。该研究项目将为学生提供一个实践研究和学习环境，并有助于网络信任劳动力的发展。

This project applies artificial-intelligence (AI) techniques to proactive vulnerability assessment (VA) in computer networks. Current library-based approach to VA does not allow the exploitation of vulnerabilities outside the library of the known attack characteristics. This proposal takes the first step toward the next generation of proactive VA software by developing advanced AI techniques that learn to attack a computer network, and hence discover its vulnerabilities and weaknesses before these weaknesses are exploited. The initial work casts VA within the framework of reinforcement learning (RL) as this approach has demonstrated previous successes for other networking problems. RL researchers study algorithms for learning high reward strategies for one or more agents based on reward signals received while interacting with an environment. For VA, the environment corresponds to a specific computer network; the reward signal provides positive reward for activity that is detrimental to a network and negative reward for activity that is detected as malicious (hence cannot harm the network). The strategy discovered by RL gives a method for one or more agents to attack the network without being detected. In this proposal, the focus is on using RL techniques to discover VA in Peer-to-Peer networks. The broader impact of this project will include bridging the gap between AI and network research communities for the purpose of providing a strong defense against network attacks. Research results will be disseminated through a website at http://www.eecs.orst.edu/~thinhq/research/AI_Security/index.html. The research project will provide a hands-on research and learning environment for students and contribute to the development of Cyber Trust workforce.