Master's Thesis - Assisting vulnerability triaging through machine learning based information processing

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Many things needs to be taken into consideration when evaluating how a particular vulnerability affects a device or system. Attack vectors, attack simplicity and consequences in terms of the CIA triad are examples of vulnerability properties that will affect the severity. Evaluating the impact and severity is part of the triaging process, when remediation priority is given to a particular vulnerability. The triage is to a large extent a manual process, making it error prone and resource consuming. The dynamics of a system, its components and how they are used in a given use case, makes it very difficult to exclude human involvement. However, machine learning and analysis of available information can be used to aid the analysts in the triaging process, allowing the process to be more accurate, repeatable, and efficient.



In this project, the most common sources used in the information gathering phase of the triaging process will be analyzed. This information will be used to provide intelligible information to the vulnerability analyst in the form of abuse cases. The different parts of the project will include,

  • Determine the proper information sources.
  • Gathering data from the sources.
  • Classification of the data and storing it in a database.
  • For known vulnerabilities, use the data and the information classification to create human readable, and easy to understand, abuse cases for the vulnerability.
  • Evaluate to which extent the triage process can benefit from the implemented approach.

Python will most likely be the chosen programming language for the implementation of the proof-of-concept in the project.



The project is suitable for 1-2 students with interest and background in security and programming. Knowledge of python, databases and machine learning (in particular natural language processing), is very relevant to the project.

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