A flexible approach for cyber threat hunting based on kernel audit records

Abstract

Hunting the advanced threats hidden in the enterprise networks has always been a complex and difficult task. Due to the variety of attacking means, it is difficult for traditional security systems to detect threats. Most existing methods analyze log records, but the amount of log records generated every day is very large. How to find the information related to the attack events quickly and effectively from massive data streams is an important problem. Considering that the knowledge graph can be used for automatic relation calculation and complex relation analysis, and can get relatively fast feedback, our work proposes to construct the knowledge graph based on kernel audit records, which fully considers the global correlation among entities observed in audit logs. We design the construction and application process of knowledge graph, which can be applied to actual threat hunting activities. Then we explore different ways to use the constructed knowledge graph for hunting actual threats in detail. Finally, we implement a LAN-wide hunting system which is convenient and flexible for security analysts. Evaluations based on the adversarial engagement designed by DARPA prove that our platform can effectively hunt sophisticated threats, quickly restore the attack path or assess the impact of attack.