UCSY's Research Repository

Feature Selection of Android Malware Detection and Analysis

Show simple item record

dc.contributor.author Hein, Chit La Pyae Myo
dc.contributor.author Myo, Khin Mar
dc.date.accessioned 2019-07-03T07:10:31Z
dc.date.available 2019-07-03T07:10:31Z
dc.date.issued 2016-02-25
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/271
dc.description.abstract Mobile malware performs malicious activities like stealing private information, sending message SMS, reading contacts can harm by exploiting data. Malware spreads around the world infects not only ends users applications but also large organizations service providers systems. Android malware is prominent to study the best classifiers that can detect these malwares effectively and accurately through selecting the most suitable permissionbased features as well as comprehensive comparison with detecting android malware. This study evaluates five machine learning classifiers, namely BayesNet, MultilayerPerceptron, Decision Tree, K-nearest neighbour, and RandomaForest. The evaluation was validated using malware data samples from the Android Malware Cantagio. This paper focused on evaluating the best feature selection to be employed in the best machine-learning classifier. en_US
dc.language.iso en en_US
dc.publisher Fourteenth International Conference On Computer Applications (ICCA 2016) en_US
dc.title Feature Selection of Android Malware Detection and Analysis en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Repository



Browse

My Account

Statistics