Name
Abstract | ||
|---|---|---|
Peer-to-Peer (P2P) live streaming traffic has been growing at a phenomenal rate over the past few years. When the original streaming content is mixed with bogus data, the corresponding P2P streaming network is being subjected to a “pollution attack.” As the content is shared by peers, the bogus data can be spread widely in minutes. In this paper, we study the impact of a pollution attack in popular streaming models, under various network settings and configurations. The study was conducted in SPoIM, our emulation of real-world P2P streaming systems under pollution attacks, through which we observed that the feasibility of the attack is sensitive to the speed at which an attacker can modify content. Our experimental results showed that different streaming approaches are more vulnerable in one network configuration than the others, and that the impact and effectiveness of the attack is not dependent on the network size, but does highly depend on the network stability and the bandwidth availability of the polluters and the source. Based the experimental results, we suggested possible improvements in streaming models to defend themselves against the pollution attack. Finally, we examined possible defense mechanisms and demonstrated the effectiveness of a reputation-based defense mechanism against a typical pollution attack. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IWQoS.2010.5542735 | Quality of Service |
Keywords | Field | DocType |
bandwidth allocation,peer-to-peer computing,telecommunication traffic,video streaming,P2P network,bandwidth availability,bogus data,live streaming traffic,network configuration,network stability,peer-to-peer network,pollution attacks | Computer security,Computer science,Bandwidth allocation,Computer network,Pollution,Bandwidth (signal processing),Emulation,Network configuration,Live streaming,Reputation,Bandwidth availability | Conference |
ISSN | ISBN | Citations |
1548-615X | 978-1-4244-5987-2 | 6 |
PageRank | References | Authors |
0.45 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Lin | 1 | 6 | 0.45 |
| Daniel Medeiros Nunes de Castro | 2 | 9 | 2.21 |
| Mea Wang | 3 | 19 | 3.53 |
| John Aycock | 4 | 351 | 33.03 |