A proposed framework for proactive vulnerability assessments in cloud deployments

Abstract

Vulnerability scanners are deployed in computer networks and software to timely identify security flaws and misconfigurations. However, cloud computing has introduced new attack vectors that requires commensurate change of vulnerability assessment strategies. To investigate the effectiveness of these scanners in cloud environments, we first conduct a quantitative security assessment of OpenStack's vulnerability lifecycle and discover severe risk levels resulting from prolonged patch release duration. More specifically, there are long time lags between OpenStack patch releases and patch inclusion in vulnerability scanning engines. This scenario introduces sufficient time for malicious actions and creation of exploits such as zero-days. Mitigating these concern requires systems with current knowledge on events within the vulnerability lifecycle. However, current vulnerability scanners are designed to depend on information about publicly announced vulnerabilities which mostly includes only vulnerability disclosure dates. Accordingly, we propose a framework that would mitigate these risks by gathering and correlating information from several security information sources including exploit databases, malware signature repositories and Bug Tracking Systems. The information is thereafter used to automatically generate plugins armed with current information about zero-day exploits and unknown vulnerabilities. We have characterized two new security metrics to describe the discovered risks.