DSpace Repository

Theme Machine Learning for smartphone security: Android botnet d

Show simple item record

dc.contributor.author REDDAH, Bayoub
dc.contributor.author DAOUDI, Nessreddin
dc.date.accessioned 2022-05-24T13:28:06Z
dc.date.available 2022-05-24T13:28:06Z
dc.date.issued 2021
dc.identifier.uri https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/1038
dc.description.abstract Android is the most used mobile operating system in the world and since it is open source, hackers exploit it to perform different attacks such as executing botnet attack which allow them to control the compromised device remotely from a Command and control (C&C) server and perform other attacks such as distributed denial of service (DDOS) from the device itself without the owners’ knowledge. The aim of our study is to find a model that allows us to detect Android botnets efficiently. Our proposed method uses a single layer and multi-layer Perceptron models trained on 342 features to classify application as benign or botnet using ICSX dataset. We obtained great results from our experimental study with an accuracy of 99%. EN_en
dc.publisher université Ghardaia EN_en
dc.subject Botnet detection, Android Botnets, Mobile Botnet, Machine learning, Perceptron, Multi-layer Perceptron, Static Analysis, Smartphone Security EN_en
dc.title Theme Machine Learning for smartphone security: Android botnet d EN_en
dc.type Thesis EN_en


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account