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Toward understanding distributed blackhole placement
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Source Workshop on Rapid Malcode archive
Proceedings of the 2004 ACM workshop on Rapid malcode table of contents
Washington DC, USA
SESSION: Session 3 table of contents
Pages: 54 - 64  
Year of Publication: 2004
ISBN:1-58113-970-5
Authors
Evan Cooke  University of Michigan, Ann Arbor, MI
Michael Bailey  University of Michigan, Ann Arbor, MI
Z. Morley Mao  University of Michigan, Ann Arbor, MI
David Watson  University of Michigan, Ann Arbor, MI
Farnam Jahanian  University of Michigan, Ann Arbor, MI
Danny McPherson  Arbor Networks
Sponsors
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 35,   Citation Count: 16
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ABSTRACT

The monitoring of unused Internet address space has been shown to be an effective method for characterizing Internet threats including Internet worms and DDOS attacks. Because there are no legitimate hosts in an unused address block, traffic must be the result of misconfiguration, backscatter from spoofed source addresses, or scanning from worms and other probing. This paper extends previous work characterizing traffic seen at specific unused address blocks by examining differences observed between these blocks. While past research has attempted to extrapolate the results from a small number of blocks to represent global Internet traffic, we present evidence that distributed address blocks observe dramatically different traffic patterns. This work uses a network of blackhole sensors which are part of the Internet Motion Sensor (IMS) collection infrastructure. These sensors are deployed in networks belonging to service providers, large enterprises, and academic institutions representing a diverse sample of the IPv4 address space. We demonstrate differences in traffic observed along three dimensions: over all protocols and services, over a specific protocol and service, and over a particular worm signature. This evidence is then combined with additional experimentation to build a list of sensor properties providing plausible explanations for these differences. Using these properties, we conclude with recommendations for the understanding the implications of sensor placement.


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  16

Collaborative Colleagues:
Evan Cooke: colleagues
Michael Bailey: colleagues
Z. Morley Mao: colleagues
David Watson: colleagues
Farnam Jahanian: colleagues
Danny McPherson: colleagues