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Bitmap algorithms for counting active flows on high-speed links
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Source IEEE/ACM Transactions on Networking (TON) archive
Volume 14 ,  Issue 5  (October 2006) table of contents
Pages: 925 - 937  
Year of Publication: 2006
ISSN:1063-6692
Authors
Cristian Estan  Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI
George Varghese  Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA
Michael Fisk  Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA
Publisher
IEEE Press  Piscataway, NJ, USA
Bibliometrics
Downloads (6 Weeks): 9,   Downloads (12 Months): 67,   Citation Count: 4
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DOI Bookmark: 10.1109/TNET.2006.882836

ABSTRACT

This paper presents a family of bitmap algorithms that address the problem of counting the number of distinct header patterns (flows) seen on a high-speed link. Such counting can be used to detect DoS attacks and port scans and to solve measurement problems. Counting is especially hard when processing must be done within a packet arrival time (8 ns at OC-768 speeds) and, hence, may perform only a small number of accesses to limited, fast memory. A naive solution that maintains a hash table requires several megabytes because the number of flows can be above a million. By contrast, our new probabilistic algorithms use little memory and are fast. The reduction in memory is particularly important for applications that run multiple concurrent counting instances. For example, we replaced the port-scan detection component of the popular intrusion detection system Snort with one of our new algorithms. This reduced memory usage on a ten minute trace from 50 to 5.6 MB while maintaining a 99.77% probability of alarming on a scan within 6 s of when the large-memory algorithm would. The best known prior algorithm (probabilistic counting) takes four times more memory on port scan detection and eight times more on a measurement application. This is possible because our algorithms can be customized to take advantage of special features such as a large number of instances that have very small counts or prior knowledge of the likely range of the count.


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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[3] C. Estan, The bmpcount library of flow counting algorithms. [Online]. Available: http://ial.ucsd.edu/bitmaps/
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[6] C. Estan, G. Varghese, and M. Fisk, Bitmap algorithms for counting active flows on high speed links Comput. Sci. Eng. Dept., Univ. California, San Diego, Tech. Rep. 0738, 2003.
 
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Collaborative Colleagues:
Cristian Estan: colleagues
George Varghese: colleagues
Michael Fisk: colleagues