Cyber Security: Nonlinear Stochastic Models for Predicting the Exploitability

• 作者: Chris. P. Tsokos Pubudu Kalpani Kaluarachchi Sasith M. Rajasooriya
• 来源期刊: 信息安全(英文)
• 学科: 医学
• 关键词: VULNERABILITY LIFECYCLE STOCHASTIC Modeling Security RISK FACTOR MARKOV Process RISK Evaluation
• 年，卷（期）: 2017,（2）
• 页码范围: 125-140
• 页数: 16页
• 分类号: R73

摘要

Obtaining complete information regarding discovered vulnerabilities looks extremely difficult. Yet, developing statistical models requires a great deal of such complete information about the vulnerabilities. In our previous studies, we introduced a new concept of “Risk Factor” of vulnerability which was calculated as a function of time. We introduced the use of Markovian approach to estimate the probability of a particular vulnerability being at a particular “state” of the vulnerability life cycle. In this study, we further develop our models, use available data sources in a probabilistic foundation to enhance the reliability and also introduce some useful new modeling strategies for vulnerability risk estimation. Finally, we present a new set of Non-Linear Statistical Models that can be used in estimating the probability of being exploited as a function of time. Our study is based on the typical security system and vulnerability data that are available. However, our methodology and system structure can be applied to a specific security system by any software engineer and using their own vulnerabilities to obtain their probability of being exploited as a function of time. This information is very important to a company’s security system in its strategic plan to monitor and improve its process for not being exploited.