Name
Abstract | ||
|---|---|---|
The growth of malicious applications poses a great threat to the Android platform. In order to detect Android malware, this paper proposes a hybrid detection method based on permission. Firstly, applications are detected according to their permissions so that benign and malicious applications can be discriminated. Secondly, suspicious applications are run in order to collect the function calls related to sensitive permissions. Then suspicious applications are represented in a vector space model and their feature vectors are calculated by TF-IDF algorithm. Finally, the detection of suspicious applications is completed via security detection techniques adopting Euclidean distance and cosine similarity. At the end of this paper, an experiment including 982 samples is used as an empirical validation. The result shows that our method has a true positive rate at 91.2 % and a false positive rate at 2.1 %. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-23829-6_40 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Android,Hybrid detection,Euclidean distance,Cosine similarity | False positive rate,Permission,Feature vector,Android (operating system),Cosine similarity,Computer science,Computer security,Euclidean distance,Android malware,Vector space model | Conference |
Volume | ISSN | Citations |
152 | 1867-8211 | 2 |
PageRank | References | Authors |
0.35 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haofeng Jiao | 1 | 2 | 0.35 |
| Xiaohong Li | 2 | 173 | 44.41 |
| Lei Zhang | 3 | 28 | 3.95 |
| guangquan xu | 4 | 6 | 3.79 |
| Zhiyong Feng | 5 | 794 | 167.21 |