Description
With the advent of the Internet, systems have become more and
more vulnerable over the years. Therefore, it is crucial to assess
how vulnerable a system is and what level of protection is required
to secure the sensitive data. Hence it gave birth to the process
of vulnerability assessment or vulnerability analysis. The whole
process has several steps. Mainly, it is required to first identify
how vulnerable a system is. Then we need to classify the identified
vulnerability in order to assess the impact that the vulnerability can
cause to the system, if breached. When it comes to the classification
of already identified vulnerabilities, there is a US based database
created in the year 2000 which is widely considered as a common
reference standard of vulnerability classification. It is known as the
National Vulnerability Database (NVD). It uses the Security Content
Automation Protocol (SCAP). The security analysts use the common
vulnerability scoring system to classify an identified vulnerability.
This system is based upon an extensive set of CVSS metrics.
The main idea behind this thesis is to automate the process
of assessing these vulnerabilities. In order to avoid the cumbersome
and time-consuming process of classifying the gravity of an
identified vulnerability, a much swifter and state of the art machine
learning based approach to actually predict and foresee the impact
of an identified vulnerability is taken. Our research mainly focuses
on machine learning based natural language processing (NLP).
The research focuses on two aspects of classifying the impact
of an identified vulnerability. Firstly, it focuses on classifying the
vulnerability impact in various classes i.e. low, medium, high,
critical. Furthermore, based on a regressive model, the system
also tries to predict the CVSS score allocated to an identified
vulnerability. The results are achieved with considerable precision
further discussed in the results section. Deep learning based neural
networks are also explored and observed i.e. how using deep
learning model impacts the results.
Conclusively, the developed model employs a very robust
machine learning based approach to classify and assess identified
vulnerabilities. Future works may include further refining the model
or use deep learning based models as the National Vulnerability
Database grows.
|
