An Approach to Detect Stegomalware in an Image using Machine Learning

Masina, Harsha Vardhan (2022) An Approach to Detect Stegomalware in an Image using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Now a days the technology had increasing very rapidly at the same time wide range of new methods are increasing the development of malicious activity by the cybercriminals with help these techniques they are gaining lots of sensitive data and credentials. There is continuous growth in cybersecurity technology same as a variety of techniques are implementing by the attacker. In that one of the important techniques is stegomalware. In this process the malware is encrypt or hide in the different forms like images, documents, and videos etc is known as stegomalware by using this kind of method they steal the critical data. This is the reason detection of malware is important it helps to detect the malware at early stage and prevent the devices from getting attacked. There are many detection approaches are established but to detect accurately the techniques of machine learning can be used because it gives very accurate results. In this paper I will using techniques of machine learning of K-NN, DT, and MLP algorithms used to detect malware with help of the datasets. In this we will evaluating the results of the model performance based on this will be able to implement confusion matrix.

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:	22 Dec 2022 11:26
Last Modified:	07 Mar 2023 16:14
URI:	https://norma.ncirl.ie/id/eprint/6021

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