Universiti Teknologi Malaysia Institutional Repository

Penetration testing procedure using machine learning

Jagamogan, Reevan Seelen and Ismail, Saiful Adli and Hassan, Noor Hafizah and Abas, Hafiza (2022) Penetration testing procedure using machine learning. In: 4th International Conference on Smart Sensors and Application, ICSSA 2022, 26 - 28 July 2022, Kuala Lumpur, Malaysia.

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Official URL: http://dx.doi.org/10.1109/ICSSA54161.2022.9870951


The main aim of this study is to determine the effectiveness of a penetration testing tool, GyoiThon as a Machine Learning tool by conducting penetration tests on websites, including the websites that have Content Management System (CMS) frameworks, to identify their vulnerabilities and assess the effectiveness of GyoiThon features in penetration testing. This experiment will determine how well the automation framework is executed for penetration testing. This research hypothesized, that if the feature of a penetration tool consists of any form of Machine Learning algorithm, the more effective the feature can search for more vulnerabilities. To achieve the aim of this paper, an experiment was conducted to evaluate the effectiveness of the two features of GyoiThon, the Default and Machine Learning modes. In the end, it was revealed that the Machine Learning mode of GyoiThon discovered more types of vulnerabilities than using the Default mode of GyoiThon, proving the hypothesis to be right.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:CMS, content management system, CVE, GyoiThon, Machine Learning, Naive Bayes, penetration testing, vulnerabilities, web application
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:98938
Deposited By: Widya Wahid
Deposited On:08 Feb 2023 17:29
Last Modified:08 Feb 2023 17:29

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