Biblio

Threat modeling is one of the best practices to secure software development. A primary challenge for using this practice is how to extract threats. Existing threat extraction methods to this purpose are mainly based on penetration tests or vulnerability databases. This imposes a non-automated timeconsuming process, which fully relies on the human knowledge and expertise. In this paper, a method is presented, which can extract the threats to a software system based on the existing description of the software behavior. We elaborately describe software behavior with sequence diagrams enriched by security relevant attributes. To enrich a sequence diagram, some attributes and their associated values are added to the diagram elements and the communication between them. We have also developed a threat knowledge base from reliable sources such as CWE and CAPEC lists. Every threat in the knowledge base is described according to its occurrence conditions in the software. To extract threats of a software system, the enriched sequence diagrams describing the software behavior are matched with the threat rules in our knowledge base using a simple inference process. Results in a set of potential threats for the software system. The proposed method is applied on a software application to extract its threats. Our case study indicates the effectiveness of the proposed method compared to other existing methods.

A fundamental problem in the specification of regulatory privacy policies such as the Health Insurance Portability and Accountability Act (HIPAA) in a computer system is to state the policies precisely, consistent with their high-level intuition. In this paper, we propose UML sequence diagrams as a practical means to graphically express privacy policies. A graphical representation allows decision-makers such as application domain experts and security architects to easily verify and confirm the expected behavior. Once intuitively confirmed, our work in this article introduces an algorithmic approach to formalizing the semantics of sequence diagrams in terms of linear temporal logic (LTL) templates. In all the templates, different semantic aspects are expressed as separate, yet simple LTL formulas that can be composed to define the complex semantics of sequence diagrams. The formalization enables us to leverage the analytical powers of automated decision procedures for LTL formulas to determine if a collection of sequence diagrams is consistent, independent, etc. and also to verify if a system design conforms to the privacy policies. We evaluate our approach by modeling and analyzing a substantial subset of HIPAA rules using sequence diagrams.