Name
Abstract | ||
|---|---|---|
Smartphones have emerged as one of the dominant computing platforms in today's era where Android has been the first choice for users as well as app developers due to its open source nature and feature rich apps. Such popularity has come hand-in-hand with an equivalent increase in malware targeting Android. Since mobile devices allow easy-to-use, touch-sensitive, and anywhere-anytime access to its resources, the mobile-specific applications like SMS, MMS, Bluetooth, e-mail, and other services may pose serious threats and lead to financial losses and privacy leakages. In recent time, high attention is drawn by the researchers for detecting Android malware; very fewer community have considered network traffic analysis in their detection models. The majority of these models have considered the detection primarily on traffic features that distinguish malware traffic from normal traffic. In this paper, we have proposed an algorithm to prioritize network traffic features with an aim to minimize the number of features to be analyzed to give high detection accuracy along with reduced training and testing time. To this extent, we have used statistical tests to rank the features. Results demonstrate that using prioritized features for detection not only reduces the training and testing time, but also gives slightly higher detection accuracy than using all the features together by measuring Fmeasure, a widely used measure for detection accuracy. The training time of 300 applications is reduced from 11.7 seconds to 5.8 seconds and testing time of 230 applications is reduced from 25.1 seconds to 17.3 seconds, hence reduction of around 50% and 31% in training time and testing time respectively. We believe this time difference will have a larger impact if there are thousands of files to be tested. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3007748.3007763 | ICDCN |
Keywords | Field | DocType |
Android, Smartphone, Malware, Network Traffic, Feature Analysis, Feature Ranking, Malware Detection, Mobile Malware | Mobile malware,Traffic analysis,Android (operating system),Computer science,Computer network,Mobile device,Malware,Pattern recognition (psychology),Statistical hypothesis testing,Bluetooth | Conference |
Citations | PageRank | References |
9 | 0.61 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anshul Arora | 1 | 32 | 2.37 |
| Sateesh K. Peddoju | 2 | 72 | 10.60 |