Threat Modeling Revisited: Improving Expressiveness of Attack

Abstract

Threat modeling plays an important role in the deployment of optimal security controls and a number of threat modeling techniques have been proposed. However, most of the existing techniques lack adequate semantics and expressiveness. This paper reviews the existing techniques and proposes threat net; a technique based on information and causality theory concepts which offers improved expressiveness and semantics of threat models. Threat net is built on Petri nets and treats every node in the threat path as a random variable, whose values include time specific attacker profile and system defense capabilities. In theory, by computing the expected value of random events one can estimate the cost of achieving a given goal. We believe that the simplicity and richness of our technique will make it attractive to security experts. In future we hope to validate threat net using case-based analysis theory.