Visible to the public Biblio

Filters: Keyword is software trustworthy security growth model  [Clear All Filters]
2020-04-03
Jabeen, Gul, Ping, Luo.  2019.  A Unified Measurable Software Trustworthy Model Based on Vulnerability Loss Speed Index. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :18—25.

As trust becomes increasingly important in the software domain. Due to its complex composite concept, people face great challenges, especially in today's dynamic and constantly changing internet technology. In addition, measuring the software trustworthiness correctly and effectively plays a significant role in gaining users trust in choosing different software. In the context of security, trust is previously measured based on the vulnerability time occurrence to predict the total number of vulnerabilities or their future occurrence time. In this study, we proposed a new unified index called "loss speed index" that integrates the most important variables of software security such as vulnerability occurrence time, number and severity loss, which are used to evaluate the overall software trust measurement. Based on this new definition, a new model called software trustworthy security growth model (STSGM) has been proposed. This paper also aims at filling the gap by addressing the severity of vulnerabilities and proposed a vulnerability severity prediction model, the results are further evaluated by STSGM to estimate the future loss speed index. Our work has several features such as: (1) It is used to predict the vulnerability severity/type in future, (2) Unlike traditional evaluation methods like expert scoring, our model uses historical data to predict the future loss speed of software, (3) The loss metric value is used to evaluate the risk associated with different software, which has a direct impact on software trustworthiness. Experiments performed on real software vulnerability datasets and its results are analyzed to check the correctness and effectiveness of the proposed model.