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 115
- Downloads 0
Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes
An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity cl...
Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images
Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagno-sis systems. Over the previous decades, researchers have proved the opportunities to automate the ini...
Measuring the Data Openness for the Open Data in Saudi Arabia e-Government – A Case Study
Conceptually, data can be found at the lowest level of abstraction from where information and knowledge are being extracted. Furthermore, data itself has no meaning, unless it’s being interpreted and transferred into inf...
Hybrid Geo-Location Routing Protocol for Indoor and Outdoor Positioning Applications
Internet of Things (IoT) essentially demands smart connectivity and contextual awareness of current networks with low power and cost effective wireless solutions. Routing is the backbone of the system controlling the flo...
Analysis of ECG Signal Processing and Filtering Algorithms
Electrocardiography (ECG) is a common technique for recording the electrical activity of human heart. Accurate computer analysis of ECG signal is challenging as it is exceedingly prone to high frequency noise and various...