Name
Abstract | ||
|---|---|---|
Malicious software (Malware) applications in Android ecosystem is one of the critical issues. Manual detection of malware is not cost-effective and cannot keep up with the fast evolution of malware development in Android. A machine learning based malware detection has attempted to automate the detection of malware in Android. In this paper, we present new Android malware detection methods. The main idea of our proposed approach is to use three different feature selection methods before malware detection model using a machine learning algorithm is constructed. We used both Malware Genome Project dataset and our own crawled dataset to show the effectiveness of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICSSA45270.2018.00023 | 2018 International Conference on Software Security and Assurance (ICSSA) |
Keywords | DocType | ISBN |
Android, Feature Selection, Machine Learning, Malware detection, Random Forest | Conference | 978-1-5386-9211-0 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taehoon Eom | 1 | 0 | 0.34 |
| Heesu Kim | 2 | 0 | 0.34 |
| SeongMo An | 3 | 2 | 2.10 |
| Jong Sou Park | 4 | 389 | 53.95 |
| Dong Seong Kim | 5 | 866 | 93.34 |