Protecting User Privacy: Advanced Techniques for detecting and preventing Keyloggers Using Machine Learning

Dasari, Ashwan Teja (2024) Protecting User Privacy: Advanced Techniques for detecting and preventing Keyloggers Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

A keylogger is a type of malware that silently monitors and logs all keystrokes that the user types on the keyboard, and it presents a grave threat to the privacy and integrity of the data of user. Conventional Anti-virus systems have limitations in detecting unknown or well-concealed keyloggers and therefore require more sophisticated detection techniques. The general goal of this study is to build novel approaches for detecting keyloggers utilizing machine learning to protect the users’ privacy. The focus goals are to establish viable and specific models for the identification of keyloggers and compare their effectiveness with prior methods; also, the study examines the implementation possibilities. The methodology used entails the data acquisition and preparation step where the different keyloggers and benign applications are obtained and preprocessed. The next step is featuring extraction where measures of the anomalous behavior of the keylogger are extracted. Different machine learning approaches, particularly reinforcement learning techniques, such as Q-learning algorithms, are applied to train models to detect keyloggers using system monitoring and API call analysis techniques. As a result, the models undergo a strict evaluation process and involve the use of measures such as accuracy, precision, and the rate of false positives to evaluate and enhance the model’s performance. The current study has shown that machine learning and reinforcement learning, especially reinforcement learning, holds massive possibilities in detecting keyloggers with relatively low false positives.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Heffernan, Niall 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 K Law > KDK Republic of Ireland > Data Protection 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:	29 Jul 2025 11:36
Last Modified:	29 Jul 2025 11:36
URI:	https://norma.ncirl.ie/id/eprint/8304

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