Mobile Malware Classification via System Calls and Permission for GPS Exploitation
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 6
Abstract
Now-a-days smartphones have been used worldwide for an effective communication which makes our life easier. Unfortunately, currently most of the cyber threats such as identity theft and mobile malwares are targeting smartphone users and based on profit gain. They spread faster among the users especially via the Android smartphones. They exploit the smartphones through many ways such as through Global Positioning System (GPS), SMS, call log, audio or image. Therefore to detect the mobile malwares, this paper presents 32 patterns of permissions and system calls for GPS exploitation by using covering algorithm. The experiment was conducted in a controlled lab environment, by using static and dynamic analyses, with 5560 of Drebin malware datasets were used as the training dataset and 500 mobile apps from Google Play Store for testing. As a result, 21 out of 500 matched with these 32 patterns. These new patterns can be used as guidance for all researchers in the same field in identifying mobile malwares and can be used as the input for a formation of a new mobile malware detection model.
Authors and Affiliations
Madihah Mohd Saudi, Muhammad ‘Afif b. Husainiamer
Computational Model for the Generalised Dispersion of Synovial Fluid
The metabolic function of synovial fluid is important to understand normal and abnormal synovial joint motion, especially if one seeks some leading causes of the degenerative joint disease. The concentration of hyaluroni...
The Application of Fuzzy Control in Water Tank Level Using Arduino
Fuzzy logic control has been successfully utilized in various industrial applications; it is generally used in complex control systems, such as chemical process control. Today, most of the fuzzy logic controls are still...
Multispectral Image Analysis using Decision Trees
Many machine learning algorithms have been used to classify pixels in Landsat imagery. The maximum likelihood classifier is the widely-accepted classifier. Non-parametric methods of classification include neural networks...
Approach for Acquiring Computer Systems to Satisfy Mission Capabilities
Defense Computer Systems developed and maintained over the years has resulted in thousands of disparate, compartmented, focused, and mission driven systems that are utilized daily for deliberate and crisis mission planni...
Weighted Marking, Clique Structure and Node-Weighted Centrality to Predict Distribution Centre’s Location in a Supply Chain Management
Despite the importance attached to the weights or strengths on the edges of a graph, a graph is only complete if it has both the combinations of nodes and edges. As such, this paper brings to bare the fact that the node-...