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 |