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Packet-dropping adversary identification for data plane security
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Source International Conference On Emerging Networking Experiments And Technologies archive
Proceedings of the 2008 ACM CoNEXT Conference table of contents
Madrid, Spain
Article No. 24  
Year of Publication: 2008
ISBN:978-1-60558-210-8
Authors
Xin Zhang  Carnegie Mellon University
Abhishek Jain  UCLA
Adrian Perrig  Carnegie Mellon University
Sponsors
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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ABSTRACT

Until recently, the design of packet dropping adversary identification protocols that are robust to both benign packet loss and malicious behavior has proven to be surprisingly elusive. In this paper, we propose a secure and practical packet-dropping adversary localization scheme that is robust and achieves a high detection rate and low communication and storage overhead -- the three key performance metrics for such protocols in realistic settings. Other recent work just optimizes either the detection rate or the communication overhead.

In this paper, we systematically explore the design space of acknowledgment-based protocols to identify a packet dropping adversary on a forwarding path. In particular, we investigate a set of basic protocols, each exemplifying a design dimension, and examine the underlying tradeoff between the performance metrics. For each basic protocol, we present both upper and lower performance bounds via theoretical analysis, and average-case results via simulations. We conclude that the proposed PAAI-1 protocol outperforms other related schemes.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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X. Zhang, A. Jain, and A. Perrig. Full version: Packet-dropping adversary identification for data plane security. Available at http://www.cs.cmu.edu/~xzhang1/doc/conext08_full.pdf.

Collaborative Colleagues:
Xin Zhang: colleagues
Abhishek Jain: colleagues
Adrian Perrig: colleagues