This paper presents a framework for security requirements elicitation and analysis. The framework is based on constructing a context for the system, representing security requirements as constraints, and developing satisfaction arguments for the security requirements. The system context is described using a problem-oriented notation, then is validated against the security requirements through construction of a satisfaction argument. The satisfaction argument consists of two parts: a formal argument that the system can meet its security requirements and a structured informal argument supporting the assumptions expressed in the formal argument. The construction of the satisfaction argument may fail, revealing either that the security requirement cannot be satisfied in the context or that the context does not contain sufficient information to develop the argument. In this case, designers and architects are asked to provide additional design information to resolve the problems. We evaluate the framework by applying it to a security requirements analysis within an air traffic control technology evaluation project.

We present a novel technique that automatically alerts authors of requirements to the presence of potentially dangerous ambiguities. We first establish the notion of nocuous ambiguities, which are those that are likely to lead to misunderstandings. We test our approach on coordination ambiguities, which occur when words such as and or are used. Our starting point is a dataset of ambiguous phrases from a requirements corpus and associated human judgements about their interpretation. We then use heuristics, based largely on word distribution information, to automatically replicate these judgements. The heuristics eliminate ambiguities which people interpret easily, leaving the nocuous ones to be analysed and rewritten by hand. We report on a series of experiments that evaluate our heuristics' performance against the human judgements. Many of our heuristics achieve high precision, and recall is greatly increased when they are used in combination

Security bug reports can describe security critical vulnerabilities in software. Bug tracking systems may contain thousands of bug reports, where relatively few of them are security related. Therefore finding unlabelled security bugs among them can be challenging. To help security engineers identify these reports quickly and accurately, text-based prediction models have been proposed. These can often mislabel security bug reports due to a number of reasons such as class imbalance, where the ratio of non-security to security bug reports is very high. More critically, we have observed that the presence of security related keywords in both security and non-security bug reports can lead to the mislabelling of security bug reports. This paper proposes FARSEC, a framework for filtering and ranking bug reports for reducing the presence of security related keywords. Before building prediction models, our framework identifies and removes non-security bug reports with security related keywords. We demonstrate that FARSEC improves the performance of text-based prediction models for security bug reports in 90% of cases. Specifically, we evaluate it with 45,940 bug reports from Chromium and four Apache projects. With our framework, we mitigate the class imbalance issue and reduce the number mislabelled security bug reports by 38%.

The Internet of Things (IoT) systems are designed and developed either as standalone applications from the ground-up or with the help of IoT middleware platforms. They are designed to support different kinds of scenarios, such as smart homes and smart cities. Thus far, privacy concerns have not been explicitly considered by IoT applications and middleware platforms. This is partly due to the lack of systematic methods for designing privacy that can guide the software development process in IoT. In this paper, we propose a set of guidelines, a privacy by-design framework, that can be used to assess privacy capabilities and gaps of existing IoT applications as well as middleware platforms. We have evaluated two open source IoT middleware platforms, namely OpenIoT and Eclipse SmartHome, to demonstrate how our framework can be used in this way.