Name
Abstract | ||
|---|---|---|
A good number of android applications are available in markets on the Internet. Among them a good number of applications are law quality apps (or malware) and therefore it is difficult for android users to decide whether particular application is malware or benign at installation time. In this paper, we propose a design of system to classify android applications into two classes i. e. malware or benign. We have used hybrid approach by combining application analysis and machine learning technique to classify the applications. Application analysis is performed by both static and live analysis techniques. Genetic algorithm based machine learning technique is used to create rules for creating rule base for the system. The system is tested with applications collected from the various markets on the Internet and two datasets. We have obtained 96.43 % detection rate to classify the applications. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-22915-7_41 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Malware analysis,Instrumentation,Rule-Based learning,Classification,Machine learning | Android (operating system),Computer security,Computer science,Android malware,Malware,Genetic algorithm,Malware analysis,The Internet | Conference |
Volume | ISSN | Citations |
536 | 1865-0929 | 2 |
PageRank | References | Authors |
0.39 | 25 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanubhai Patel | 1 | 4 | 1.11 |
| Bharat Buddadev | 2 | 2 | 0.39 |