Mohd Faizal Ab Razak
Faculty of Computer Systems & Software Engineering, University Malaysia Pahang

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Selecting Root Exploit Features Using Flying Animal-Inspired Decision Ahmad Firdaus; Mohd Faizal Ab Razak; Wan Isni Sofiah Wan Din; Danakorn Nincarean; Shahreen Kasim; Tole Sutikno; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 4: December 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (465.202 KB) | DOI: 10.52549/ijeei.v7i4.1146

Abstract

Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the nucleus of the operating system known as kernel to bypass the Android security mechanisms. Once it attacks and resides in the kernel, it is able to install other possible types of malware to the Android devices. In order to detect root exploit, it is important to investigate its features to assist machine learning to predict it accurately. This study proposes flying animal-inspired (1) bat, 2) firefly, and 3) bee) methods to search automatically the exclusive features, then utilizes these flying animal-inspired decision features to improve the machine learning prediction. Furthermore, a boosting method (Adaboost) boosts the multilayer perceptron (MLP) potential to a stronger classification. The evaluation jotted the best result is from bee search, which recorded 91.48 percent in accuracy, 82.2 percent in true positive rate, and 0.1 percent false positive rate.