Enhancing Cybersecurity Posture through Web-based Automated Google Dorking

Majithia, Diti (2024) Enhancing Cybersecurity Posture through Web-based Automated Google Dorking. Masters thesis, Dublin, National College of Ireland.

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Abstract

The growing complexity of cyber threats drove the realization that the tools used in vulnerability assessment had to be more efficient. One of the principal tools, used only by security researchers, was identified as Google Dorking. Executed manually, it was slow and error-prone. The WAGDT - Dorkinator project was undertaken to address the disadvantages of manual methods by introducing a user-friendly automated solution. The tool was tested for effectiveness by comparing it with a different existing tool in terms of accuracy, efficiency, and user satisfaction. The Dorkinator project was developed to provide augmentation in digital reconnaissance by decreasing manual input and increasing security posture. While designing the tool, emphasis was given to automated complex Google Dorking queries. Thus, it would simplify the information-gathering phase in penetration testing. Implemented using efficient and scalable technologies at the time of its development, the tool focuses on user-friendly design and real-time data processing. In terms of evaluation, Dorkinator demonstrated higher usability, faster execution of queries, and high satisfaction compared to the existing tool, Investigator. The results put forward the potential of Dorkinator to change the state of cybersecurity practice by democratizing the process of vulnerability discovery. This work contributed to the automation of more advanced search techniques and is expected to lead to enhancements in the detection of vulnerabilities. It was acknowledged, however, that further testing in other varied environments and the integration of other advanced features, such as machine learning, were yet to be done. Commercial viability, in some sense, is already very plausible, suggesting further avenues of development and application.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas UNSPECIFIED
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 > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	30 Jul 2025 10:48
Last Modified:	30 Jul 2025 10:48
URI:	https://norma.ncirl.ie/id/eprint/8334

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