Multi Classifier Models using Machine Learning Techniques for Malware Detection

-, Janius Christabel Joseph (2021) Multi Classifier Models using Machine Learning Techniques for Malware Detection. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration manual) Download (2MB) | Preview

Abstract

One of the most serious problems facing almost all sectors and industries is the battle against malware as new variants are being developed every day.
Malicious software is a serious threat to organizations, both public and private, as well as individuals at all levels. Because malware characteristics are constantly evolving, most existing solutions or anti-malware detectors are ineffective at identifying new strains of malware.
Most cutting-edge research today uses machine learning for malware detection. But the drawback with these methods is that they are mostly focused on binary classification and do not identify the kind of malware which has infected the systems. The approach used in this research aims to use a multi classifier to detect and classify malware.
Malware classification is approached using two techniques of binary and multi-class problems. The binary classification includes the differentiation between malicious and benign classes whereas the multi-classification includes classifying the malicious malware into Virus, Trojan, Spyware, Worms, Ransomware, and Adware type. Supervised learning approach and machine learning models like Random Forest model, Decision tree model, Support vector machine model, Naïve Bayes model, and K-Nearest Neighbour model is used for the classification of malware. The results show that Random Forest performs well in terms of Binary classification and the multi-classification problem with an accuracy of 95% and 91% respectively.

Item Type:	Thesis (Masters)
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:	19 Dec 2022 16:40
Last Modified:	07 Mar 2023 16:53
URI:	https://norma.ncirl.ie/id/eprint/6007

Actions (login required)

View Item