Effective approach for Malware Detection using Machine Learning and Deep Learning for IoT-Devices

Galinde, Devika Rajiv (2023) Effective approach for Malware Detection using Machine Learning and Deep Learning for IoT-Devices. Masters thesis, Dublin, National College of Ireland.

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Abstract

The Internet of Things (IoT) is an amazing innovative technology created and developed by humans. As, the technology is growing fast, the new devices are coming in the market with excellent features and hence there is high possibility of the applications or the IoT devices getting vulnerable to the cyber treat to the environments. The IoT devices are smart devices which communicates through the internet and therefore, it possessing high chances of getting trapped by the cyber criminals. The related work in this research focuses on the malware detection with multiple Deep Learning(DL) and Machine Learning (ML) models. As, there are various malware variants in the environment, it is the primary focus to prevent it from any attack causing severe loss to the industries. This research aimed in detecting malware with a high accuracy using ML and DL models. For Decision Tree the accuracy is 0.999 ~ 1. The Decision tree seems to be over fitted and so Random Forest algorithm is introduced and the accuracy achieved by RF is 0.998% .

Item Type:	Thesis (Masters)
Supervisors:	Name Email Moldovan, Arghir-Nicolae 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 T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things 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:	15 Jan 2024 16:23
Last Modified:	15 Jan 2024 16:23
URI:	https://norma.ncirl.ie/id/eprint/6912

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