Enhancing Social Engineering Detection Using Behavioural Biometrics and Machine Learning

Uday, Pranav (2024) Enhancing Social Engineering Detection Using Behavioural Biometrics and Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

In order to enhance the detection of social engineering through the use ofbehavioural biometrics, it is necessary to make use of various human behavioural features in order to identify and combat potential threats. The most recent study has an emphasis on a number of methods, including keystroke dynamics and the merging of social behavioural information, both of which have the potential to significantly improve detection capability and robustness. In this paper, we suggest the use of artificial intelligence to develop behaviour-based detection algorithms for cybersecurity. This approach tackles both the key concerns of protecting users against complicated SE attacks and ensuring that authentication goes smoothly. In conclusion, this research attempts to improve cybersecurity detection by combining behavioural principles with artificial intelligence technology. This is done in response to the growing number of security concerns. Keystroke dynamics and touch analytics are used to train a variety of machine learning models, which are then constructed from scratch. After evaluating the performance of the models, it was determined that the random forest model produced the highest level of accuracy.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Moldovan, Arghir Nicolae UNSPECIFIED
Uncontrolled Keywords:	Cyber Security; Application Design; Machine Learning; Biometrics
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:	28 Jul 2025 14:43
Last Modified:	28 Jul 2025 14:43
URI:	https://norma.ncirl.ie/id/eprint/8271

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