{"title":"A First Look at the Usability of OpenVAS Vulnerability Scanner","authors":"M. U. Aksu, Enes ALTUNCU, K. Bicakci","doi":"10.14722/usec.2019.23026","DOIUrl":"https://doi.org/10.14722/usec.2019.23026","url":null,"abstract":"Vulnerability scanning is a fundamental step for assuring system security. It is also an integral component of IT system risk assessment to manage the identified vulnerabilities in a timely and prioritized way. It is critical that the tools for vulnerability scanning are usable so that cybersecurity practitioners get the most out of them. In this work, we evaluate the usability of a commonly used open source vulnerability scanning tool − OpenVAS 9.0. For this purpose, we carry out expertbased and user-based testings. Expert-based testing is carried out by employing the heuristic analysis and cognitive walkthrough approaches. User-based testing is performed by selecting 10 cybersecurity experts as participants. As a result, we identify pitfalls that lead to insecurity or false sense of security and suggest improvements to overcome them. We also discuss the effectiveness of the methodologies employed for usability testing. Lastly, a set of heuristics compiled from the existing work and adapted to our case is provided to be reused in similar studies.","PeriodicalId":215851,"journal":{"name":"Proceedings 2019 Workshop on Usable Security","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131902343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Safi, Abhiditya Jha, Malak Eihab Aly, Xinru Page, S. Patil, P. Wisniewski
{"title":"Will They Share? Predicting Location Sharing Behaviors of Smartphone Users through Self-Reflection on Past Privacy Behaviors","authors":"M. Safi, Abhiditya Jha, Malak Eihab Aly, Xinru Page, S. Patil, P. Wisniewski","doi":"10.14722/usec.2019.23014","DOIUrl":"https://doi.org/10.14722/usec.2019.23014","url":null,"abstract":"—Location sharing is a particularly sensitive type of online information disclosure. To explain this behavior, we compared the effectiveness of using self-report measures drawn from the literature, behavioral data collected from mobile phones, and a new type of measure that represents a hybrid of self-report and behavioral data to contextualize users’ attitudes toward their past location sharing behaviors. This new measure was based on a reflective learning paradigm, where one reflects on past behavior to inform future behavior. Based on a study of Android smartphone users (N=114), we found that the construct ‘FYI About Myself’ and our new reflective measure of one’s comfort with sharing location with apps on the smartphone were the best predictors of location sharing behavior. Surprisingly, Behavioral Intention, a commonly used proxy for actual behavior, was not a significant predictor. These results have important implications for privacy research and designing systems to meet users’ location sharing privacy needs.","PeriodicalId":215851,"journal":{"name":"Proceedings 2019 Workshop on Usable Security","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124072378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}