Name
Title | ||
|---|---|---|
A multi-granularity heuristic-combining approach for censorship circumvention activity identification. |
Abstract | ||
|---|---|---|
Identifying censorship circumvention network traffic has become an important task for preventing abuse of those tools. However, traditional flow-based methods have drawbacks in high false positive rate, and they fail to exploit useful hidden features. In this paper, we propose a novel feature extraction method for censorship circumvention activity identification, which extracts features from multi-granularity, and it uses a heuristic-combining approach to make the final decision. Moreover, unlike traditional approaches, which classify on an individual flow or a packet, the proposed method examines on a new granularity. We present an implementation based on the proposed method, and the results are presented to demonstrate the effectiveness of our method. In comparison to the traditional flow-based methods, the proposed strategy has a slightly lower overall accuracy rate than flow-based approaches; however, its average false positive rate is significantly lower than the traditional method. Copyright © 2016 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1002/sec.1524 | Security and Communication Networks |
Keywords | Field | DocType |
multi-granularity,heuristic-combining,feature extraction,censorship circumvention | False positive rate,Data mining,Heuristic,Censorship,Computer security,Computer science,Network packet,Exploit,Feature extraction,Granularity | Journal |
Volume | Issue | ISSN |
9 | 16 | 1939-0114 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongliu Zhuo | 1 | 3 | 2.09 |
| Xiao-song Zhang | 2 | 305 | 45.10 |
| Ruixing Li | 3 | 0 | 0.34 |
| Ting Chen | 4 | 153 | 12.80 |
| Jing-Zhong Zhang | 5 | 137 | 16.54 |