Hypothesis Exploration for Malware Detection Using Planning

In this paper we apply AI planning to address the hypothesis exploration problem and provide assistance to network administrators in detecting malware based on unreliable observations derived from network traffic.Building on the already established characterization and use of AI planning for similar problems, we propose a formulation of the hypothesis generation problem for malware detection as an AI planning problem with temporally extended goals and actions costs. Furthermore, we propose a notion of hypothesis ``plausibility'' under unreliable observations, which we model as plan quality. We then show that in the presence of unreliable observations, simply finding one most ``plausible'' hypothesis, although challenging, is not sufficient for effective malware detection. To that end, we propose a method for applying a state-of-the-art planner within a principled exploration process, to generate multiple distinct high-quality plans. We experimentally evaluate this approach by generating random problems of varying hardness both with respect to the number of observations, as well as the degree of unreliability. Based on these experiments, we argue that our approach presents a significant improvement over prior work that are focused on finding a single optimal plan, and that our hypothesis exploration application can motivate the development of new planners capable of generating the top high-quality plans.