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Register a new userTowards a More Systematic Approach to Secure Systems Design and Analysis
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Added by
Uwe Aickelin
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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The task of designing secure software systems is fraught with uncertainty, as data on uncommon attacks is limited, costs are difficult to estimate, and technology and tools are continually changing. Consequently, experts may interpret the security risks posed to a system in different ways, leading to variation in assessment. This paper presents research into measuring the variability in decision making between security professionals, with the ultimate goal of improving the quality of security advice given to software system designers. A set of thirty nine cyber-security experts took part in an exercise in which they independently assessed a realistic system scenario. This study quantifies agreement in the opinions of experts, examines methods of aggregating opinions, and produces an assessment of attacks from ratings of their components. We show that when aggregated, a coherent consensus view of security emerges which can be used to inform decisions made during systems design.
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Significant developments have taken place over the past few years in the area of vehicular communication (VC) systems. Now, it is well understood in the community that security and protection of private user information are a prerequisite for the deployment of the technology. This is so, precisely because the benefits of VC systems, with the mission to enhance transportation safety and efficiency, are at stake. Without the integration of strong and practical security and privacy enhancing mechanisms, VC systems could be disrupted or disabled, even by relatively unsophisticated attackers. We address this problem within the SeVeCom project, having developed a security architecture that provides a comprehensive and practical solution. We present our results in a set of two papers in this issue. In this first one, we analyze threats and types of adversaries, we identify security and privacy requirements, and we present a spectrum of mechanisms to secure VC systems. We provide a solution that can be quickly adopted and deployed. In the second paper, we present our progress towards the implementation of our architecture and results on the performance of the secure VC system, along with a discussion of upcoming research challenges and our related current results.
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Mobile application security has been one of the major areas of security research in the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However, existing tools, and specifically, static analysis tools, trade soundness of the analysis for precision and performance, and are hence soundy. Unfortunately, the specific unsound choices or flaws in the design of these tools are often not known or well-documented, leading to a misplaced confidence among researchers, developers, and users. This paper proposes the Mutation-based soundness evaluation ($mu$SE) framework, which systematically evaluates Android static analysis tools to discover, document, and fix, flaws, by leveraging the well-founded practice of mutation analysis. We implement $mu$SE as a semi-automated framework, and apply it to a set of prominent Android static analysis tools that detect private data leaks in apps. As the result of an in-depth analysis of one of the major tools, we discover 13 undocumented flaws. More importantly, we discover that all 13 flaws propagate to tools that inherit the flawed tool. We successfully fix one of the flaws in cooperation with the tool developers. Our results motivate the urgent need for systematic discovery and documentation of unsound choices in soundy tools, and demonstrate the opportunities in leveraging mutation testing in achieving this goal.
The Android mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app and recording every call to sensitive APIs. Later, one can use this information to (a) prevent calls to other sensitive APIs (those not previously recorded) or (b) run the dynamic analysis tools again in a different version of the app -- in order to identify possible malicious behavior. Although the use of dynamic analysis for mining Android sandboxes has been empirically investigated before, little is known about the potential benefits of combining static analysis with the mining sandbox approach for identifying malicious behavior. As such, in this paper we present the results of two empirical studies: The first is a non-exact replication of a previous research work from Bao et al., which compares the performance of test case generation tools for mining Android sandboxes. The second is a new experiment to investigate the implications of using taint analysis algorithms to complement the mining sandbox approach in the task to identify malicious behavior. Our study brings several findings. For instance, the first study reveals that a static analysis component of DroidFax (a tool used for instrumenting Android apps in the Bao et al. study) contributes substantially to the performance of the dynamic analysis tools explored in the previous work. The results of the second study show that taint analysis is also practical to complement the mining sandboxes approach, improve the performance of the later strategy in at most 28.57%.
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