Intelligent Hybrid Approach for Android Malware Detection based on Permissions and API Calls
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 6
Abstract
Android malware is rapidly becoming a potential threat to users. The number of Android malware is growing exponentially; they become significantly sophisticated and cause potential financial and information losses for users. Hence, there is a need for effective and efficient techniques to detect the Android malware applications. This paper proposes an intelligent hybrid approach for Android malware detection using the permissions and API calls in the Android application. The proposed approach consists of two steps. The first step involves finding the most significant permissions and Application Programming Interfaces (API) calls that leads to efficient discrimination between the malware and good ware applications. For this purpose, two features selection algorithms, Information Gain (IG) and Pearson CorrCoef (PC) are employed to rank the individual permissions and API’s calls based on their importance for classification. In the second step, the proposed new hybrid approach for Android malware detection based on the combination of the Adaptive neural fuzzy Inference System (ANFIS) with the Particle Swarm Optimization (PSO), is employed to differentiate between the malware and goodware Android applications (apps). The PSO is intelligently utilized to optimize the ANFIS parameters by tuning its membership functions to generate reliable and more precise fuzzy rules for Android apps classification. Using a dataset consists of 250 goodware and 250 malware apps collected from different recourse, the conducted experiments show that the suggested method for Android malware detection is effective and achieved an accuracy of 89%.
Authors and Affiliations
Altyeb Altaher, Omar Mohammed Barukab
Keywords
Related Articles
- EP ID EP259520
- DOI 10.14569/IJACSA.2017.080608
- Views 110
- Downloads 0
Physical Activity Identification using Supervised Machine Learning and based on Pulse Rate
Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and d...
User Interface Design of E-Learning System for Functionally Illiterate People
Among different type of illiterate people, the print illiterates suffer most from getting crucial information passed around the society. Many print illiterate people are found in the developing countries and in many case...
Crowd Mobility Analysis using WiFi Sniffers
Wi-fi enabled devices such as today’s smart-phones are regularly in-search for connectivity. They continuously send management frames called Probe Requests searching for previ-ously accessed networks. These frames contai...
Modelling the Enterprise Architecture Implementation in the Public Sector using HOT-Fit Framework
Enterprise architecture is very important to the public sector’s IT systems that are developed, organized, scaled up, maintained and strategized. Despite an extensive literature, the research of enterprise architecture i...
Text Steganography using Extensions Kashida based on the Moon and Sun Letters Concept
Existing steganography methods are still lacking in terms of capacity. Hence, a new steganography method for Arabic text is proposed. The method hides secret information bits within Arabic letters using two features, whi...