Risk-based Network Vulnerability Prioritization

This dissertation investigates the problem of efficiently and effectively prioritizing a vulnerability risk in a computer networking system. Vulnerability prioritization is one of the most challenging issues in vulnerability management, which affects allocating preventive and defensive resources in a computer networking system. Due to the large number of identified vulnerabilities, it is very challenging to remediate them all in a timely fashion. Thus, an efficient and effective vulnerability prioritization framework is required. To deal with this challenge, this dissertation proposes a novel risk-based vulnerability prioritization framework that integrates the recent artificial intelligence techniques (i.e., neuro-symbolic computing and logic reasoning). The proposed work enhances the vulnerability management process by prioritizing vulnerabilities with high risk by refining the initial risk assessment with the network constraints. This dissertation is organized as follows. The first part of this dissertation presents the overview of the proposed risk-based vulnerability prioritization framework, which contains two stages. The second part of the dissertation investigates vulnerability risk features in a computer networking system. The third part proposes the first stage of this framework, a vulnerability risk assessment model. The proposed assessment model captures the pattern of vulnerability risk features to provide a more comprehensive risk assessment for a vulnerability. The fourth part proposes the second stage of this framework, a vulnerability prioritization reasoning engine. This reasoning engine derives network constraints from interactions between vulnerabilities and network environment elements based on network and system setups. This proposed framework assesses a vulnerability in a computer networking system based on its actual security impact by refining the initial risk assessment with the network constraints.