Improving cyber-attack predictions through information foraging
Adam Dalton, Bonnie Jean Dorr, Leon Liang, Kristy Hollingshead · 2017
This paper describes how information foraging is useful in the implementation of new algorithms to anticipate cyber attacks. The exploration of publicly available data has been used to predict events in the socio-political domain, but the adversarial and covert behavior of actors in cyber security creates additional challenges. This paper describes a framework for Information Foraging for Algorithm Discovery (IFAD) that addresses standard data-science issues of volume and variety, by balancing human intuition with automation, and thus taking initial steps toward supporting the increasing need for rapid analysis of, and tool development for, big data. Our results demonstrate that cognitive augmentation, and information foraging in particular, is useful in the development of tools to anticipate cyber attacks using publicly available data.