Name
Abstract | ||
|---|---|---|
Existing Android malware detection approaches mostly have concentrated on superficial features such as requested or used permissions, which can't reflect the essential differences between benign apps and malware. In this paper, we propose a quantitative calculation model of application risks based on the key observation that the essential differences between benign apps and malware actually lie in the way how permissions are used, or rather the way how their corresponding permission methods are used. Specifically, we employ a fine-grained analysis on Android application risks. We firstly classify application risks into five specific categories and then introduce comprehensive risk, which is computed based on the former five, to describe the overall risk of an application. Given that users' risk preference and risk-bearing ability are naturally fuzzy, we design and implement a fuzzy logic system to calculate the comprehensive risk. On the basis of the quantitative calculation model, we propose a risk classification based approach for Android malware detection. The experiments show that our approach can achieve high accuracy with a low false positive rate using the RandomForest algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3837/tiis.2017.02.018 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
Android,malware detection,risk,machine learning,fuzzy logic | Computer science,Computer network,Android malware,Operating system | Journal |
Volume | Issue | ISSN |
11 | 2 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilin Ye | 1 | 0 | 0.34 |
| Lifa Wu | 2 | 47 | 15.85 |
| Zheng Hong | 3 | 14 | 3.78 |
| Kangyu Huang | 4 | 0 | 0.34 |