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.
Highlights
People utilize mobile devices in their daily activities to connect, online and communicate
Root exploit is an application software that takes over the kernel of the Android operating system to gain root privileges
Nine subsets were used for training set and one subset is for testing set
Summary
People utilize mobile devices in their daily activities to connect, online and communicate This situation provides an opportunity for the attacker to develop root exploit to compromise victim’s Android device for money or private purposes. The number of root exploits increasing from time to time because of the homebrew communities These communities are the people that find multiple ways to break the Android kernel to obtain a customized version of Android. This leaves an opportunity for root exploit writers to wait for the homebrew community to discover new ways to gain control of the Android’s kernel [4]. In order to detect root exploit, security practitioners conducted the two types of malware analysis; 1) dynamic, and 2) static analysis
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More From: Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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