Automated Classification and Mitigation of Cybersecurity Vulnerabilities

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Aghaei, E. (2022). Automated Classification and Mitigation of Cybersecurity Vulnerabilities. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

With the widespread use of computers and networks today, cybersecurity has emerged as a crucial concern for many businesses as they fight with growing cyber threats by vulnerability exploitation. To identify and mitigate zero-day or unpatched vulnerabilities, intensive defensive measures are required, which calls for a thorough understanding of vulnerability characteristics and threat behavior from several angles. This compels enterprises to spend a considerable amount of money to safeguard their infrastructure from cyberattacks, relying on the costly, ineffective, error-prone, and slow process of experts' input. Therefore, security automation has been a solution for many business owners in the battle against the growing number of cyber threats by vulnerability exploitation.In recent years, advanced AI technologies in text analytics are gaining wide attention due to their success in a wide range of applications for automating cybersecurity processes. The modern text analytics architectures have been built in novel ways for a variety of applications, assisting cybersecurity professionals in developing resilient mechanisms against threats. Utilizing such technologies can therefore be a viable approach for processing, understanding, and predicting vulnerabilities that are typically reported through unstructured text.This dissertation utilizes a variety of technologies including deep learning (DL) models, natural language processing (NLP) approaches, and information retrieval (IR) techniques to build a series of models that are able to effectively and efficiently parse, assess, analyze, and mitigate the vulnerabilities based on their textual descriptions reported in Common Vulnerabilities and Exposures (CVE) format. Particularly, it offers a cybersecurity language model, as the core component, which is then utilized for characterizing the vulnerabilities as well as retrieving the corresponding course of defense actions. As a result of this work, enterprises and cybersecurity researchers will be able to automatically process domain-specific texts, classify vulnerabilities to cybersecurity standards to obtain high-level knowledge, and retrieve the course of defense actions for the underlying threats.

Details
Author

Aghaei, Ehsan

Title

Automated Classification and Mitigation of Cybersecurity Vulnerabilities

Physical Description

1 online resource (167 pages) : PDF

Date

2022

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

With the widespread use of computers and networks today, cybersecurity has emerged as a crucial concern for many businesses as they fight with growing cyber threats by vulnerability exploitation. To identify and mitigate zero-day or unpatched vulnerabilities, intensive defensive measures are required, which calls for a thorough understanding of vulnerability characteristics and threat behavior from several angles. This compels enterprises to spend a considerable amount of money to safeguard their infrastructure from cyberattacks, relying on the costly, ineffective, error-prone, and slow process of experts' input. Therefore, security automation has been a solution for many business owners in the battle against the growing number of cyber threats by vulnerability exploitation.In recent years, advanced AI technologies in text analytics are gaining wide attention due to their success in a wide range of applications for automating cybersecurity processes. The modern text analytics architectures have been built in novel ways for a variety of applications, assisting cybersecurity professionals in developing resilient mechanisms against threats. Utilizing such technologies can therefore be a viable approach for processing, understanding, and predicting vulnerabilities that are typically reported through unstructured text.This dissertation utilizes a variety of technologies including deep learning (DL) models, natural language processing (NLP) approaches, and information retrieval (IR) techniques to build a series of models that are able to effectively and efficiently parse, assess, analyze, and mitigate the vulnerabilities based on their textual descriptions reported in Common Vulnerabilities and Exposures (CVE) format. Particularly, it offers a cybersecurity language model, as the core component, which is then utilized for characterizing the vulnerabilities as well as retrieving the corresponding course of defense actions. As a result of this work, enterprises and cybersecurity researchers will be able to automatically process domain-specific texts, classify vulnerabilities to cybersecurity standards to obtain high-level knowledge, and retrieve the course of defense actions for the underlying threats.

Genre

doctoral dissertations

Subjects--Topics

Computer science

Degree

Ph.D.

Keywords

Cyber Automation
Cyber Text Analytics
Language Model
Natural Language Processing
Vulnerability Classification
Vulnerability Mitigation

Subject Area

Computer Science

Advisor(s)

Aghaei, Ehsan

Committee Members

Niu, Xi
Chu, Bei-Tseng (Bill)
Shadid, Waseem
Raja, Yasin

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2022.

Rights Statement

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Aghaei_uncc_0694D_13223

Permalink

http://hdl.handle.net/20.500.13093/etd:3166