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Detection of malware in a file using Machine Learning

Murali, Saishankar (2020) Detection of malware in a file using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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In today's world the technology is increasing very rapidly and so is the increasing the development of malware and malicious activity through which the cybercriminals are gaining a lot on sensitive information and credentials. The innovations of the latest technology motivate cybercriminals to create malicious code and also to perform them through which they steal data and perform abnormal activities in the technologies. This is the reason malware detection is very important so that we can detect the malware at the early stage and prevent the devices from getting attacked. There are many detection methods established but to enhance them more further machine learning technique can be used because it is accurate and efficient. In this paper I will be using machine learning techniques, K-means clustering algorithm to detect the malware with the help of dataset which will be trained, K-means clustering algorithm will be used because it is accurate and gives the output as expected because of the mathematical operation it consists. In this, we will be evaluating the performance based on the algorithm, Detection, and the dataset that will be used, and also confusion matrix will be performed to get the false positive and negative results.
Keywords: - Machine Learning, Malware, Detection, Algorithm, Dataset, Private Information.

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
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Dan English
Date Deposited: 27 Jan 2021 17:02
Last Modified: 27 Jan 2021 17:02

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