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 95
- Downloads 0
A Novel Student Risk Identification Model using Machine Learning Approach
This research work aim at addressing issues in detecting student, who are at risk of failing to complete their course. The conceptual design presents a solution for efficient learning in non-existence of data from previo...
Circular Calibration of Depth Extraction in Stereo Configuration
Lens distortion is defined as departure from rectilinear projection of an imaging system which affects the accuracy of almost all vision applications. This work addresses the problem of distortion with investigating the...
Solving the Free Clustered TSP Using a Memetic Algorithm
The free clustered travelling salesman problem (FCTSP) is an extension of the classical travelling salesman problem where the set of vertices is partitioned into clusters, and the task is to find a minimum cost Hamiltoni...
Demand Response Programs Significance, Challenges and Worldwide Scope in Maintaining Power System Stability
In order to cope up the continuously increasing electric demand, Governments are forced to invest on Renewable Energy (RE) sources due to scarcity of fossil fuels (such as coal, gas and oil), high costs associated with i...
Concepts and Tools for Protecting Sensitive Data in the IT Industry: A Review of Trends, Challenges and Mechanisms for Data-Protection
Advancements in storage, dissemination and access of multimedia data content on the Internet continues to grow at exponential rates, while individuals, organizations and governments spend huge efforts to exert their fing...