Biblio

Choosing how to write natural language scenarios is challenging, because stakeholders may over-generalize their descriptions or overlook or be unaware of alternate scenarios. In security, for example, this can result in weak security constraints that are too general, or missing constraints. Another challenge is that analysts are unclear on where to stop generating new scenarios. In this paper, we introduce the Multifactor Quality Method (MQM) to help requirements analysts to empirically collect system constraints in scenarios based on elicited expert preferences. The method combines quantitative statistical analysis to measure system quality with qualitative coding to extract new requirements. The method is bootstrapped with minimal analyst expertise in the domain affected by the quality area, and then guides an analyst toward selecting expert-recommended requirements to monotonically increase system quality. We report the results of applying the method to security. This include 550 requirements elicited from 69 security experts during a bootstrapping stage, and subsequent evaluation of these results in a verification stage with 45 security experts to measure the overall improvement of the new requirements. Security experts in our studies have an average of 10 years of experience. Our results show that using our method, we detect an increase in the security quality ratings collected in the verification stage. Finally, we discuss how our proposed method helps to improve security requirements elicitation, analysis, and measurement. 

Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of distributed knowledge, we investigate the challenge of developing a security requirements rule base that mimics human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules underpinning the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The proposed approach is tested by evaluating 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The initial results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts’ ratings. 

Mobile and web applications increasingly leverage service-oriented architectures in which developers integrate third-party services into end user applications. This includes identity management, mapping and navigation, cloud storage, and advertising services, among others. While service reuse reduces development time, it introduces new privacy and security risks due to data repurposing and over-collection as data is shared among multiple parties who lack transparency into thirdparty data practices. To address this challenge, we propose new techniques based on Description Logic (DL) for modeling multiparty data flow requirements and verifying the purpose specification and collection and use limitation principles, which are prominent privacy properties found in international standards and guidelines. We evaluate our techniques in an empirical case study that examines the data practices of the Waze mobile application and three of their service providers: Facebook Login, Amazon Web Services (a cloud storage provider), and Flurry.com (a popular mobile analytics and advertising platform). The study results include detected conflicts and violations of the principles as well as two patterns for balancing privacy and data use flexibility in requirements specifications. Analysis of automation reasoning over the DL models show that reasoning over complex compositions of multi-party systems is feasible within exponential asymptotic timeframes proportional to the policy size, the number of expressed data, and orthogonal to the number of conflicts found.

As information security became an increasing concern for software developers and users, requirements engineering (RE) researchers brought new insight to security requirements. Security requirements aim to address security at the early stages of system design while accommodating the complex needs of different stakeholders. Meanwhile, other research communities, such as usable privacy and security, have also examined these requirements with specialized goal to make security more usable for stakeholders from product owners, to system users and administrators. In this paper we report results from conducting a literature survey to compare security requirements research from RE Conferences with the Symposium on Usable Privacy and Security (SOUPS). We report similarities between the two research areas, such as common goals, technical definitions, research problems, and directions. Further, we clarify the differences between these two communities to understand how they can leverage each other’s insights. From our analysis, we recommend new directions in security requirements research mainly to expand the meaning of security requirements in RE to reflect the technological advancements that the broader field of security is experiencing. These recommendations to encourage crosscollaboration with other communities are not limited to the security requirements area; in fact, we believe they can be generalized to other areas of RE. 

As information systems become increasingly interdependent, there is an increased need to share cybersecurity data across government agencies and companies, and within and across industrial sectors. This sharing includes threat, vulnerability and incident reporting data, among other data. For cyberattacks that include sociotechnical vectors, such as phishing or watering hole attacks, this increased sharing could expose customer and employee personal data to increased privacy risk. In the US, privacy risk arises when the government voluntarily receives data from companies without meaningful consent from individuals, or without a lawful procedure that protects an individual's right to due process. In this paper, we describe a study to examine the trade-off between the need for potentially sensitive data, which we call incident data usage, and the perceived privacy risk of sharing that data with the government. The study is comprised of two parts: a data usage estimate built from a survey of 76 security professionals with mean eight years' experience; and a privacy risk estimate that measures privacy risk using an ordinal likelihood scale and nominal data types in factorial vignettes. The privacy risk estimate also factors in data purposes with different levels of societal benefit, including terrorism, imminent threat of death, economic harm, and loss of intellectual property. The results show which data types are high-usage, low-risk versus those that are low-usage, high-risk. We discuss the implications of these results and recommend future work to improve privacy when data must be shared despite the increased risk to privacy.

Security analysis requires specialized knowledge to align threats and vulnerabilities in information technology. To identify mitigations, analysts need to understand how threats, vulnerabilities, and mitigations are composed together to yield security requirements. Despite abundant guidance in the form of checklists and controls about how to secure systems, evidence suggests that security experts do not apply these checklists. Instead, they rely on their prior knowledge and experience to identify security vulnerabilities. To better understand the different effects of checklists, design analysis, and expertise, we conducted a series of interviews to capture and encode the decisionmaking process of security experts and novices during three security analysis exercises. Participants were asked to analyze three kinds of artifacts: source code, data flow diagrams, and network diagrams, for vulnerabilities, and then to apply a requirements checklist to demonstrate their ability to mitigate vulnerabilities. We framed our study using Situation Awareness, which is a theory about human perception that was used to elicit interviewee responses. The responses were then analyzed using coding theory and grounded analysis. Our results include decision-making patterns that characterize how analysts perceive, comprehend, and project future threats against a system, and how these patterns relate to selecting security mitigations. Based on this analysis, we discovered new theory to measure how security experts and novices apply attack models and how structured and unstructured analysis enables increasing security requirements coverage. We highlight the role of expertise level and requirements composition in affecting security decision-making and we discuss how our method produced new hypotheses about security analysis and decisionmaking.

Research shows that commonly accepted security requirements   are  not  generally  applied  in  practice.  Instead  of relying on requirements checklists, security experts rely on their expertise and background knowledge to identify security vulnerabilities.  To  understand  the  gap  between  available checklists  and  practice,  we  conducted  a  series  of  interviews  to encode   the   decision-making   process   of  security   experts   and novices during security requirements analysis. Participants were asked to analyze two types of artifacts: source code, and network diagrams  for  vulnerabilities  and  to  apply  a  requirements checklist to mitigate some of those vulnerabilities.  We framed our study using Situation Awareness—a cognitive theory from psychology—to   elicit  responses   that  we  later  analyzed   using coding theory and grounded analysis.  We report our preliminary results of analyzing two interviews that reveal possible decision- making patterns that could characterize how analysts perceive, comprehend   and  project  future  threats  which  leads  them  to decide upon requirements  and their specifications,  in addition, to how  experts  use  assumptions  to  overcome  ambiguity  in specifications.  Our goal is to build a model that researchers  can use to evaluate their security requirements methods against how experts transition through different situation awareness levels in their decision-making  process.

Information system developers and administrators often overlook critical security requirements and best practices. This may be due to lack of tools and techniques that allow practitioners to tailor security knowledge to their particular context. In order to explore the impact of new security methods, we must improve our ability to study the impact of security tools and methods on software and system development. In this paper, we present early findings of an experiment to assess the extent to which the number and type of examples used in security training stimuli can impact security problem solving. To motivate this research, we formulate hypotheses from analogical transfer theory in psychology. The independent variables include number of problem surfaces and schemas, and the dependent variable is the answer accuracy. Our study results do not show a statistically significant difference in performance when the number and types of examples are varied. We discuss the limitations, threats to validity and opportunities for future studies in this area.

Security analysis requires some degree of knowledge to align threats to vulnerabilities in information technology. Despite the abundance of security requirements, the evidence suggests that security experts are not applying these checklists. Instead, they default to their background knowledge to identify security vulnerabilities. To better understand the different effects of security checklists, analysis and expertise, we conducted a series of interviews to capture and encode the decisionmaking process of security experts and novices during three security requirements analysis exercises. Participants were asked to analyze three kinds of artifacts: source code, data flow diagrams, and network diagrams, for vulnerabilities, and then to apply a requirements checklist to demonstrate their ability to mitigate vulnerabilities. We framed our study using Situation Awareness theory to elicit responses that were analyzed using coding theory and grounded analysis. Our results include decision-making patterns that characterize how analysts perceive, comprehend and project future threats, and how these patterns relate to selecting security mitigations. Based on this analysis, we discovered new theory to measure how security experts and novices apply attack models and how structured and unstructured analysis enables increasing security requirements coverage. We discuss suggestions of how our method could be adapted and applied to improve training and education instruments of security analysts.

Security requirements analysis depends on how well-trained analysts perceive security risk, understand the impact of various vulnerabilities, and mitigate threats. When systems are composed of multiple machines, configurations, and software components that interact with each other, risk perception must account for the composition of security requirements. In this paper, we report on how changes to security requirements affect analysts risk perceptions and their decisions about how to modify the requirements to reach adequate security levels. We conducted two user surveys of 174 participants wherein participants assess security levels across 64 factorial vignettes. We analyzed the survey results using multi-level modeling to test for the effect of security requirements composition on participants’ overall security adequacy ratings and on their ratings of individual requirements. We accompanied this analysis with grounded analysis of elicited requirements aimed at lowering the security risk. Our results suggest that requirements composition affects experts’ adequacy ratings on security requirements. In addition, we identified three categories of requirements modifications, called refinements, replacements and reinforcements, and we measured how these categories compare with overall perceived security risk. Finally, we discuss the future impact of our work in security requirements assessment practice.

Mobile and web applications increasingly leverage service-oriented architectures in which developers integrate third-party services into end user applications. This includes identity management, mapping and navigation, cloud storage, and advertising services, among others. While service reuse reduces development time, it introduces new privacy and security risks due to data repurposing and over-collection as data is shared among multiple parties who lack transparency into third-party data practices. To address this challenge, we propose new techniques based on Description Logic (DL) for modeling multi-party data flow requirements and verifying the purpose specification and collection and use limitation principles, which are prominent privacy properties found in international standards and guidelines. We evaluate our techniques in an empirical case study that examines the data practices of the Waze mobile application and three of their service providers: Facebook Login, Amazon Web Services (a cloud storage provider), and Flurry.com (a popular mobile analytics and advertising platform). The study results include detected conflicts and violations of the principles as well as two patterns for balancing privacy and data use flexibility in requirements specifications. Analysis of automation reasoning over the DL models show that reasoning over complex compositions of multi-party systems is feasible within exponential asymptotic timeframes proportional to the policy size, the number of expressed data, and orthogonal to the number of conflicts found.