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.

No abstract

No abstract

No abstract

We have developed a tool, enbug, that intentionally induces errors into software in a controlled fashion. The robustness of students' code can be challenged by presenting exotic failure scenarios for testing, without Herculean efforts on the part of teaching assistants or instructors. Enbug also has applications in computer security and secure software courses, by being able to inject specific flaws into existing software for students to locate and exploit. The implementation of enbug is an example of tool reuse, through the automated (ab)use of a debugger.