Biblio

Zero-day attacks continue to challenge the enterprise network security defense. A zero-day attack path is formed when a multi-step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. In this paper, we propose a probabilistic approach to identify zero-day attack paths and implement a prototype system named ZePro. An object instance graph is first built from system calls to capture the intrusion propagation. To further reveal the zero-day attack paths hiding in the instance graph, our system constructs an instance-graph-based Bayesian network. By leveraging intrusion evidence, the Bayesian network can quantitatively compute the probabilities of object instances being infected. The object instances with high infection probabilities reveal themselves and form the zero-day attack paths. The experiment results show that our system can effectively identify zero-day attack paths.

Cyber SA is described as the current and predictive knowledge of cyberspace in relation to the Network, Missions and Threats across friendly, neutral and adversary forces. While this model provides a good high-level understanding of Cyber SA, it does not contain actionable information to help inform the development of capabilities to improve SA. In this paper, we present a systematic, human-centered process that uses a card sort methodology to understand and conceptualize Senior Leader Cyber SA requirements. From the data collected, we were able to build a hierarchy of high- and low- priority Cyber SA information, as well as uncover items that represent high levels of disagreement with and across organizations. The findings of this study serve as a first step in developing a better understanding of what Cyber SA means to Senior Leaders, and can inform the development of future capabilities to improve their SA and Mission Performance.