Biblio

Today's software is full of security vulnerabilities that invite attack. Attackers are especially drawn to software systems containing sensitive data. For such systems, this paper presents a modeling approach especially suited for Serum or other forms of agile development to identify and reduce the attack surface. The latter arises due to the locations containing sensitive data within the software system that are reachable by attackers. The approach reduces the attack surface by changing the design so that the number of such locations is reduced. The approach performs these changes on a visual model of the software system. The changes are then considered for application to the actual system to improve its security.

The growth of the internet has brought along positive gains such as the emergence of a highly interconnected world. However, on the flip side, there has been a growing concern on how secure distributed systems can be built effectively and tested for security vulnerabilities prior to deployment. Developing a secure software product calls for a deep technical understanding of some complex issues with regards to the software and its operating environment, as well as embracing a systematic approach of analyzing the software. This paper proposes a method for identifying software security vulnerabilities from software requirement specifications written in Structured Object-oriented Formal Language (SOFL). Our proposed methodology leverages on the concept of providing an early focus on security by identifying potential security vulnerabilities at the requirement analysis and verification phase of the software development life cycle.

Detecting software security vulnerabilities and distinguishing vulnerable from non-vulnerable code is anything but simple. Most of the time, vulnerabilities remain undisclosed until they are exposed, for instance, by an attack during the software operational phase. Software metrics are widely-used indicators of software quality, but the question is whether they can be used to distinguish vulnerable software units from the non-vulnerable ones during development. In this paper, we perform an exploratory study on software metrics, their interdependency, and their relation with security vulnerabilities. We aim at understanding: i) the correlation between software architectural characteristics, represented in the form of software metrics, and the number of vulnerabilities; and ii) which are the most informative and discriminative metrics that allow identifying vulnerable units of code. To achieve these goals, we use, respectively, correlation coefficients and heuristic search techniques. Our analysis is carried out on a dataset that includes software metrics and reported security vulnerabilities, exposed by security attacks, for all functions, classes, and files of five widely used projects. Results show: i) a strong correlation between several project-level metrics and the number of vulnerabilities, ii) the possibility of using a group of metrics, at both file and function levels, to distinguish vulnerable and non-vulnerable code with a high level of accuracy.