Intrusion Detection of Kernal-Rootkits in Android Devices using Machine Learning– Random Forest

Satheesh Kumar, Rohith (2022) Intrusion Detection of Kernal-Rootkits in Android Devices using Machine Learning– Random Forest. Masters thesis, Dublin, National College of Ireland.

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Abstract

Due to the prevalence of sensitive information on Smartphones, rootkits provide a major risk. Rootkits on Android-based smartphones have access to features that aren't accessible on PCs, such as GPS, the battery, and the microphone and speaker. This makes them particularly dangerous. Smartphone users are at greater danger of infection as open source and unlicensed third-party platforms and applications are being used. A kernel-level rootkit's threat to Android's operating system is also examined in depth in this study, as is the potential use of a system call to discriminate between calls from a regular app and those coming from an infected one.

Automated rootkit detection is possible using random forest, an approach that relies on prior data. For the purpose of detecting the rootkit, it takes use of datasets produced by feeding data and collecting system calls from infected and non-infected operating systems. A root kit identification algorithm for Android-based systems is trained using this dataset.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Kernel rootkits; Android; Random forest technique; Machine learning
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 Q Science > QA Mathematics > Computer software > Mobile Phone Applications T Technology > T Technology (General) > Information Technology > Computer software > Mobile Phone Applications
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	29 Dec 2022 15:28
Last Modified:	07 Mar 2023 11:01
URI:	https://norma.ncirl.ie/id/eprint/6054

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