The continuous increase of sophisticated cyber security risks exposed to the public, industry, and government through the web, mobile devices, social media, as well as targeted attacks via state-sponsored cyberespionage, clearly show the need for software security. Security testing is one of the most important practices to assure an acceptable level of security. However, security testers face the problem of determining the tests that are most likely to reveal severe security vulnerabilities. This is important in order to focus security testing on the most risky aspects of a system.In response to this challenge, the security testing community has proposed an approach to support security testing with security risk assessment (risk-driven security testing). In general, the purpose of risk-driven security testing is to focus the testing on the most severe security risks that the system under test is exposed to. However, current approaches carry out risk assessment at a high-level of abstraction (for example, business level) and then perform the testing accordingly. This is a disadvantage from a testing perspective because it leaves a gap between the risks and the test cases which are defined at a low-level of abstraction (for example, implementation level). This gap makes it difficult to identify exactly where in the system risks occur, and exactly how the risks should be tested. This also indicates that current approaches focus on risk-driven test planning at a high-level of abstraction for test management purposes, and do not necessarily focus on guiding the tester in designing test cases that have the ability to reveal vulnerabilities causing the most severe risks.This thesis proposes a model-based approach to risk-driven security testing, named CORAL, which is specifically developed to help security testers select and design test cases based on the available risk picture. The CORAL approach consists of seven steps supported by a risk analysis language. The risk analysis language is a modeling language based on UML interactions, and is formalized by an abstract syntax and a schematically defined natural-language semantics.As part of the development and evaluation process of the CORAL approach we carried out three industrial case studies. In the first two case studies, we investigated how risk assessment may be used to identify security test cases, as well as how security testing may be used to improve security risk analysis results. The experiences we obtained from these two industrial case studies helped us to, among other things, shape the CORAL approach. In the third case study we carried out the CORAL approach in an industrial setting in order to evaluate its applicability. The results indicate that CORAL supports security testers in producing risk models that are valid and directly testable. By directly testable risk models we mean risk models that can be reused and specified as test cases based on the interactions in the risk models. This, in turn, helps testers to select and design test cases according to t...

STAIRS is a formal approach to system development with UML 2.1 sequence diagrams that supports an incremental and modular development process. STAIRS is underpinned by denotational and operational semantics that have been proved to be equivalent. STAIRS is more expressive than most approaches with a formal notion of refinement. STAIRS supports a stepwise refinement process under which trace properties as well as trace-set properties are preserved. This paper demonstrates the potential of STAIRS in this respect, in particular that refinement in STAIRS preserves adherence to information flow properties as well as policies.

No abstract

Part 1: The International Cross Domain Conference (CD-ARES 2016)International audienceAs a basis for offering policy and setting tariffs, cyber-insurance carriers need to assess the cyber risk of companies. This paper explores the challenges insurance companies face in assessing cyber risk, based on literature and interviews with representatives from insurers. The interview subjects represent insurance companies offering cyber-insurance in a market where this is a new and unknown product. They have limited historical data, with few examples of incidents leading to payout. This lack of experience and data, together with the need for an efficient sales process, highly impacts their approach to risk assessment. Two options for improving the ability to perform thorough yet efficient assessments of cyber risk are explored in this paper: basing analysis on reusable sector-specific risk models, and including managed security service providers (MSSPs) in the value chain