Visible to the public Biblio

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Hibshi, Hanan, Slavin, Rocky, Niu, Jianwei, Breaux, Travis D.  2014.  Rethinking Security Requirements in RE Research.

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 cross- collaboration with other communities are not limited to the security requirements area; in fact, we believe they can be generalized to other areas of RE. 

Conference Paper
Rao, Ashwini, Hibshi, Hanan, Breaux, Travis, Lehker, Jean-Michel, Niu, Jianwei.  2014.  Less is More?: Investigating the Role of Examples in Security Studies Using Analogical Transfer Proceedings of the 2014 Symposium and Bootcamp on the Science of Security. :7:1–7:12.

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.

Bhatia, Jaspreet, Breaux, Travis D., Friedberg, Liora, Hibshi, Hanan, Smullen, Daniel.  2016.  Privacy Risk in Cybersecurity Data Sharing. Proceedings of the 2016 ACM on Workshop on Information Sharing and Collaborative Security. :57–64.

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.

Hibshi, Hanan.  2016.  Systematic Analysis of Qualitative Data in Security. Proceedings of the Symposium and Bootcamp on the Science of Security. :52–52.

This tutorial will introduce participants to Grounded Theory, which is a qualitative framework to discover new theory from an empirical analysis of data. This form of analysis is particularly useful when analyzing text, audio or video artifacts that lack structure, but contain rich descriptions. We will frame Grounded Theory in the context of qualitative methods and case studies, which complement quantitative methods, such as controlled experiments and simulations. We will contrast the approaches developed by Glaser and Strauss, and introduce coding theory - the most prominent qualitative method for performing analysis to discover Grounded Theory. Topics include coding frames, first- and second-cycle coding, and saturation. We will use examples from security interview scripts to teach participants: developing a coding frame, coding a source document to discover relationships in the data, developing heuristics to resolve ambiguities between codes, and performing second-cycle coding to discover relationships within categories. Then, participants will learn how to discover theory from coded data. Participants will further learn about inter-rater reliability statistics, including Cohen's and Fleiss' Kappa, Krippendorf's Alpha, and Vanbelle's Index. Finally, we will review how to present Grounded Theory results in publications, including how to describe the methodology, report observations, and describe threats to validity.

Hibshi, Hanan.  2016.  Systematic Analysis of Qualitative Data in Security. Proceedings of the Symposium and Bootcamp on the Science of Security. :52–52.
This tutorial will introduce participants to Grounded Theory, which is a qualitative framework to discover new theory from an empirical analysis of data. This form of analysis is particularly useful when analyzing text, audio or video artifacts that lack structure, but contain rich descriptions. We will frame Grounded Theory in the context of qualitative methods and case studies, which complement quantitative methods, such as controlled experiments and simulations. We will contrast the approaches developed by Glaser and Strauss, and introduce coding theory - the most prominent qualitative method for performing analysis to discover Grounded Theory. Topics include coding frames, first- and second-cycle coding, and saturation. We will use examples from security interview scripts to teach participants: developing a coding frame, coding a source document to discover relationships in the data, developing heuristics to resolve ambiguities between codes, and performing second-cycle coding to discover relationships within categories. Then, participants will learn how to discover theory from coded data. Participants will further learn about inter-rater reliability statistics, including Cohen's and Fleiss' Kappa, Krippendorf's Alpha, and Vanbelle's Index. Finally, we will review how to present Grounded Theory results in publications, including how to describe the methodology, report observations, and describe threats to validity.