Predicting Attacks on Vulnerabilities using Random Forest

Lopes, Sarell (2020) Predicting Attacks on Vulnerabilities using Random Forest. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration manual) Download (5MB) | Preview

Abstract

Vulnerability assessment is an integral part of information security. CVSS is a globally accepted standard for calculating risk and prioritising the vulnerabilities during the IT system assessment. Automated vulnerability management systems rely on CVSS for their patching processes and ranking weaknesses. CVSS have received some disapproval from the researchers for its limitation to asses the severity factor of vulnerability. The goal of this research is to combine external factors. Proof of exploits and attack signatures along with CVSS impact metrics, privilege attribute and user interactions as features for the Random Forest algorithm to evaluate the proposal of predicting an attack on a vulnerability.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	03 Apr 2020 15:03
Last Modified:	03 Apr 2020 15:03
URI:	https://norma.ncirl.ie/id/eprint/4177

Actions (login required)

View Item