Towards Approaches to Continuous Assessment of Cyber Risk in Security of Computer Networks

TL;DR

This paper reviews current methods and challenges in continuous cyber risk assessment for computer networks, emphasizing data collection, risk scoring, and the need for more rigorous, integrated approaches.

Contribution

It highlights the limitations of existing risk metrics and discusses research challenges in developing more accurate, comprehensive risk assessment techniques.

Findings

Current risk metrics are overly simplistic and subjective.

Integration of heterogeneous data remains a major challenge.

Existing approaches lack rigorous, dynamic risk scoring methods.

Abstract

We review the current status and research challenges in the area of cyber security often called continuous monitoring and risk scoring (CMRS). We focus on two most salient aspects of CMRS. First, continuous collection of data through automated feeds; hence the term continuous monitoring. Typical data collected for continuous monitoring purposes include network traffic information as well as host information from host-based agents. Second, analysis of the collected data in order to assess the risks - the risk scoring. This assessment may include flagging especially egregious vulnerabilities and exposures, or computing metrics that provide an overall characterization of the network's risk level. Currently used risk metrics are often simple sums or counts of vulnerabilities and missing patches. The research challenges pertaining to CMRS fall mainly into two categories. The first centers on the problem of integrating and fusing highly heterogeneous information. The second group of challenges is the lack of rigorous approaches to computing risk. Existing risk scoring algorithms remain limited to ad hoc heuristics such as simple sums of vulnerability scores or counts of things like missing patches or open ports, etc. Weaknesses and potentially misleading nature of such metrics are well recognized. For example, the individual vulnerability scores are dangerously reliant on subjective, human, qualitative input, potentially inaccurate and expensive to obtain. Further, the total number of vulnerabilities may matters far less than how vulnerabilities are distributed over hosts, or over time. Similarly, neither topology of the network nor the roles and dynamics of inter-host interactions are considered by simple sums of vulnerabilities or missing patches.