Mitigating Vulnerability Risks in Cybersecurity Using Predictive Measures

Richmond Adebiaye


The number of vulnerability attacks and the ease with which an attack can be perpetrated have increased as the software industry and Internet use have grown. Researchers have discovered a lack of established procedures for analysis and collection of data errors generated during software development. Under such conditions, from a software developer’s perspective, the probability of releasing secured products may not be feasible, as vulnerabilities are likely to be discovered. Given the fact that there is no guaranteed vulnerability risk free software currently in existence, it is critical to understand vulnerability risks prediction and prevention measures. This study examines vulnerability risks using statistical predictive design measures based on software characteristics. The study tests the severity, frequency and diversity of vulnerability risks. Using a survey methodology to collect data from IT practitioners, and analyzing publicly available vulnerability risks information, prediction capabilities were examined and tested. The study showed cogent insights and provided clear perspectives of vulnerability risks and how software characteristics can be used as predictive measures to identify security holes. The study will ultimately help IT and Information Security experts to understand frequency and severity of vulnerability risks and proffer solutions during software development.


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