@inproceedings{f004b798d47f4bdcb43485bc7008cd22,
title = "Comparative Evaluation of Ensemble Learning and Supervised Learning in Android Malwares Using Network-Based Analysis",
abstract = "With the prevalence of mobile devices, the security threats are growing in number and seriousness. Among the mobile operating systems, Google{\textquoteright}s Android has been attacked more than others have. From April 2013 until June 2013, the number of malwares were doubled for the Android. In this paper, we evaluate the mobile malwares detection using the ensemble learning and supervised learning. Furthermore, we compare the two learning approaches based on the experimental results. We compared our experimental results with a similar work. The network traffic generated by mobile malwares are analyzed. We use 600 malware samples from the MalGenome data sample to build the dataset. We use two versions of random forest algorithm as our evaluating algorithm, ensemble learning and supervised learning. The empirical results show that the ensemble learning improves the detection of the Android malwares. The ensemble learning achieved 99.6 \% of true positive rate while the supervised learning attained 99.4 \%.",
keywords = "Android, ensemble learning, Malgenome, mobile malware, network-based analysis, supervised learning",
author = "Ali Feizollah and Anuar, \{Nor Badrul\} and Rosli Salleh and Fairuz Amalina",
year = "2014",
month = nov,
day = "14",
doi = "10.1007/978-3-319-07674-4\_95",
language = "English",
isbn = "9783319076737",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Cham",
pages = "1025–1035",
editor = "HA Sulaiman and MA Othman and MFI Othman and Y AbdRahim and NC Pee",
booktitle = "Advanced Computer And Communication Engineering Technology",
address = "Switzerland",
note = "2014 International Conference on Communication and Computer Engineering, ICCCE 2014 ; Conference date: 23-09-2014 Through 25-09-2014",
}