Malware Detection in Android platform using DNN

Wakhare, Akshay Ashok (2021) Malware Detection in Android platform using DNN. Masters thesis, Dublin, National College of Ireland.

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Abstract

The android platform market is growing exponentially and so the attacks on android platform are increased. The attacks usually performed by installing an android application with malicious code inside the application. On initializing the malicious application an attacker is able to get device access, network information and so on. In the past, many researchers have performed research on this problem. This research is performed aiming to solve and add extra layer of defence in android platform using deep learning technology.

The research is carried out by developing hybrid malware detection models in which static model was developed using static features of an android application such as manifest permissions, Intents and API calls whereas the dynamic model was developed using dynamic features such as system calls and system binder calls. The recurrent neural network particularly Long Short-Term Memory technique is utilized to developed both the models. Both the static and dynamic models are trained and the efficiency of the models is analysed using confusion matric and roc & auc scores. The developed models will be used in the organisation to add an extra layer of security in their current working mobile threat detection system.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Android malware detection; Hybrid malware detection; LSTM; Recurrent neural network
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 > 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:	Clara Chan
Date Deposited:	02 Nov 2021 13:38
Last Modified:	02 Nov 2021 13:38
URI:	https://norma.ncirl.ie/id/eprint/5130

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