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We propose and apply a requirements engineering approach that focuses on security and privacy properties and takes into account various stakeholder interests. The proposed methodology facilitates the integration of security and privacy by design into the requirements engineering process. Thus, specific, detailed security and privacy requirements can be implemented from the very beginning of a software project. The method is applied to an exemplary application scenario in the logistics industry. The approach includes the application of threat and risk rating methodologies, a technique to derive technical requirements from legal texts, as well as a matching process to avoid duplication and accumulate all essential requirements.
We present an analysis of how to determine security requirements for software that controls routing decisions in the distribution of discrete physical goods. Requirements are derived from stakeholder interests and threat scenarios. Three deployment scenarios are discussed: cloud and hybrid deployment as well as on-premise installation for legacy sites.
Conducting surveillance impact assessment is the first step to solve the "Who monitors the monitor?" problem. Since the surveillance impacts on different dimensions of privacy and society are always changing, measuring compliance and impact through metrics can ensure the negative consequences are minimized to acceptable levels. To develop metrics systematically for surveillance impact assessment, we follow the top-down process of the Goal/Question/Metric paradigm: 1) establish goals through the social impact model, 2) generate questions through the dimensions of surveillance activities, and 3) develop metrics through the scales of measure. With respect to the three factors of impact magnitude: the strength of sources, the immediacy of sources, and the number of sources, we generate questions concerning surveillance activities: by whom, for whom, why, when, where, of what, and how, and develop metrics with the scales of measure: the nominal scale, the ordinal scale, the interval scale, and the ratio scale. In addition to compliance assessment and impact assessment, the developed metrics have the potential to address the power imbalance problem through sousveillance, which employs surveillance to control and redirect the impact exposures.
We compared vulnerable and fixed versions of the source code of 50 different PHP open source projects based on CVE reports for SQL injection vulnerabilities. We scanned the source code with commercial and open source tools for static code analysis. Our results show that five current state-of-the-art tools have issues correctly marking vulnerable and safe code. We identify 25 code patterns that are not detected as a vulnerability by at least one of the tools and 6 code patterns that are mistakenly reported as a vulnerability that cannot be confirmed by manual code inspection. Knowledge of the patterns could help vendors of static code analysis tools, and software developers could be instructed to avoid patterns that confuse automated tools.
We present source code patterns that are difficult for modern static code analysis tools. Our study comprises 50 different open source projects in both a vulnerable and a fixed version for XSS vulnerabilities reported with CVE IDs over a period of seven years. We used three commercial and two open source static code analysis tools. Based on the reported vulnerabilities we discovered code patterns that appear to be difficult to classify by static analysis. The results show that code analysis tools are helpful, but still have problems with specific source code patterns. These patterns should be a focus in training for developers.
With the increased deployment of biometric authentication systems, some security concerns have also arisen. In particular, presentation attacks directed to the capture device pose a severe threat. In order to prevent them, liveness features such as the blood flow can be utilised to develop presentation attack detection (PAD) mechanisms. In this context, laser speckle contrast imaging (LSCI) is a technology widely used in biomedical applications in order to visualise blood flow. We therefore propose a fingerprint PAD method based on textural information extracted from pre-processed LSCI images. Subsequently, a support vector machine is used for classification. In the experiments conducted on a database comprising 32 different artefacts, the results show that the proposed approach classifies correctly all bona fides. However, the LSCI technology experiences difficulties with thin and transparent overlay attacks.
We present an approach to reduce the complexity of adjusting privacy preferences for multiple online social networks. To achieve this, we quantify the effect on privacy for choices that users make, and simplify configuration by introducing privacy configuration as a service. We present an algorithm that effectively measures privacy and adjusts privacy settings across social networks. The aim is to configure privacy with one click.