Detection of Ransomware Attacks using Supervised Machine Learning

Abu Saad, Laith Ahmad (2022) Detection of Ransomware Attacks using Supervised Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

The continuous growth and advancements of technology-based products in several industries and sectors of the economy and society have also been plagued by an increasing number of cyber-attacks targeted at compromising systems, stealing sensitive information, etc. These attacks take several shapes including malware attacks, phishing, and distributed denial of service (DDoS) amongst others. Consequently, ransomware has been identified as a major type of malware attack. Therefore, it has become pertinent that more attention is drawn to creating tools and techniques to specifically detect ransomware attacks. Several research exists that has focused on techniques such as Machine Learning (ML) and deep learning algorithms for detecting malware, however, few have been centred on ransomware detection. As such, in this research, the Random Forest classifier and Logistic regression classifier as machine learning techniques are explored to determine their accuracy in the detection of ransomware attacks. The logistic regression model achieved an accuracy of about 74% with a precision and recall of around 74% of average each. The random forest model outperformed the logistic regression, achieving a near 100% accuracy, precision, and recall, with only 2 misclassifications in the confusion matrix out of 350 thousand rows on the test dataset.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Salahuddin, Jawad UNSPECIFIED
Uncontrolled Keywords:	Ransomware; machine learning; malware; random forest; logistic regression
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:	Tamara Malone
Date Deposited:	21 Apr 2023 17:03
Last Modified:	28 Apr 2023 15:12
URI:	https://norma.ncirl.ie/id/eprint/6503

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