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Hop-count filtering: an effective defense against spoofed DDoS traffic
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Source Conference on Computer and Communications Security archive
Proceedings of the 10th ACM conference on Computer and communications security table of contents
Washington D.C., USA
SESSION: DOS protection table of contents
Pages: 30 - 41  
Year of Publication: 2003
ISBN:1-58113-738-9
Authors
Cheng Jin  Caltech, Pasadena, CA
Haining Wang  College of William and Mary, Williamsburg, VA
Kang G. Shin  University of Michigan, Ann Arbor, MI
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 30,   Downloads (12 Months): 224,   Citation Count: 42
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ABSTRACT

IP spoofing has been exploited by Distributed Denial of Service (DDoS) attacks to (1) conceal flooding sources and localities in flooding traffic, and (2) coax legitimate hosts into becoming reflectors, redirecting and amplifying flooding traffic. Thus, the ability to filter spoofed IP packets near victims is essential to their own protection as well as to their avoidance of becoming involuntary DoS reflectors. Although an attacker can forge any field in the IP header, he or she cannot falsify the number of hops an IP packet takes to reach its destination. This hop-count information can be inferred from the Time-to-Live (TTL) value in the IP header. Using a mapping between IP addresses and their hop-counts to an Internet server, the server can distinguish spoofed IP packets from legitimate ones. Base on this observation, we present a novel filtering technique that is immediately deployable to weed out spoofed IP packets. Through analysis using network measurement data, we show that Hop-Count Filtering (HCF) can identify close to 90% of spoofed IP packets, and then discard them with little collateral damage. We implement and evaluate HCF in the Linux kernel, demonstrating its benefits using experimental measurements.


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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CITED BY  42

Collaborative Colleagues:
Cheng Jin: colleagues
Haining Wang: colleagues
Kang G. Shin: colleagues