Estimating Impact and Frequency of Risks to Safety and Mission Critical Systems Using CVSS

Many safety and mission critical systems depend on the correct and secure operation of both supportive and core software systems. E.g., both the safety of personnel and the effective execution of core missions on an oil platform depend on the correct recording storing, transfer and interpretation of data, such as that for the Logging While Drilling (LWD) and Measurement While Drilling (MWD) subsystems. Here, data is recorded on site, packaged and then transferred to an on-shore operational centre. Today, the data is transferred on dedicated communication channels to ensure a secure and safe transfer, free from deliberately and accidental faults. However, as the cost control is ever more important some of the transfer will be over remotely accessible infrastructure in the future. Thus, communication will be prone to known security vulnerabilities exploitable by outsiders. This paper presents a model that estimates risk level of known vulnerabilities as a combination of frequency and impact estimates derived from the Common Vulnerability Scoring System (CVSS). The model is implemented as a Bayesian Belief Network (BBN)

Quantifying security risk level from CVSS estimates of frequency and impact

Modern society relies on and profits from well-balanced computerized systems. Each of these systems has a core mission such as the correct and safe operation of safety critical systems or innovative and effective operation of e-commerce systems. It might be said that the success of these systems depends on their mission. Although the concept of “well-balanced” has a slightly different meaning for each of these two categories of systems, both have to meet customer needs, deliver capabilities and functions according to expectations and generate revenue to sustain today’s highly competitive market. Tighter financial constraints are forcing safety critical systems away from dedicated and expensive communication regimes, such as the ownership and operation of dedicated communication links, towards reliance on third parties and standardized means of communication. As a consequence, knowledge about their internal structures and operations is more widely and publicly available and this can make them more prone to security attacks. These systems are, therefore, moving towards a remotely exploitable environment and the risks associated with this must be controlled. Risk management is a good tool for controlling risk but it has the inherent challenge of quantitatively estimating frequency and impact in an accurate and trustworthy way. Quantifying the frequency and impact of potential security threats requires experience-based data which is limited and rarely reusable because it involves company confidential data. Therefore, there is a need for publicly available data sources that can be used in risk estimation. This paper presents a risk estimation model that makes use of one such data source, the Common Vulnerability Scoring System (CVSS). The CVSS Risk Level Estimation Model estimates a security risk level from vulnerability information as a combination of frequency and impact estimates derived from the CVSS. It is implemented as a Bayesian Belief Network (BBN) topology, which allows not only the use of CVSS-based estimates but also the combination of disparate information sources and, thus, provides the ability to use whatever risk information that is available. The model is demonstrated using a safety- and mission-critical system for drilling operational support, the Measurement and Logging While Drilling (M/LWD) system