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Reversible sketches: enabling monitoring and analysis over high-speed data streams
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Source IEEE/ACM Transactions on Networking (TON) archive
Volume 15 ,  Issue 5  (October 2007) table of contents
Pages: 1059 - 1072  
Year of Publication: 2007
ISSN:1063-6692
Authors
Robert Schweller  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Zhichun Li  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Yan Chen  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Yan Gao  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Ashish Gupta  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Yin Zhang  Department of Computer Science, University of Texas at Austin, TX
Peter A. Dinda  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Ming-Yang Kao  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Gokhan Memik  Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Publisher
IEEE Press  Piscataway, NJ, USA
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DOI Bookmark: 10.1109/TNET.2007.896150

ABSTRACT

A key function for network traffic monitoring and analysis is the ability to perform aggregate queries over multiple data streams. Change detection is an important primitive which can be extended to construct many aggregate queries. The recently proposed sketches are among the very few that can detect heavy changes online for high speed links, and thus support various aggregate queries in both temporal and spatial domains. However, it does not preserve the keys (e. g., source IP address) of flows, making it difficult to reconstruct the desired set of anomalous keys.

To address this challenge, we propose the reversible sketch data structure along with reverse hashing algorithms to infer the keys of culprit flows. There are two phases. The first operates online, recording the packet stream in a compact representation with negligible extra memory and few extra memory accesses. Our prototype single FPGA board implementation can achieve a throughput of over 16 Gb/s for 40-byte packet streams (the worst case). The second phase identifies heavy changes and their keys from the representation in nearly real time. We evaluate our scheme using traces from large edge routers with OC-12 or higher links. Both the analytical and experimental results show that we are able to achieve online traffic monitoring and accurate change/intrusion detection over massive data streams on high speed links, all in a manner that scales to large key space size. To the best of our knowledge, our system is the first to achieve these properties simultaneously.


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.

1
 
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[2] G. Cormode and S. Muthukrishnan, "What's new: Finding significant differences in network data streams," in Proc. IEEE INFOCOM, 2004.
3
 
4
[4] G. Cormode, F. Korn, D. Srivastava, and S. Muthukrishnan, "Finding hierarchical heavy hitters in data streams," in Proc. VLDB, 2003.
5
 
6
 
7
[7] R. S. Tsay, "Time series model specification in the presence outliers," J. Amer. Statistical Assoc., vol. 81, pp. 132-141, 1986.
 
8
[8] G. Cormode and S. Muthukrishnan, "Improved data stream summaries: the count-min sketch and its applications," DIMACS, Tech. Rep. 2003-20, 2003.
 
9
 
10
[10] A. Gilbert, Y. Kotidis, S. Muthukrishnan, and M. Strauss, "Quick-SAND: Quick summary and analysis of network data," DIMACS, Tech. Rep. 2001-43, 2001.
 
11
 
12
 
13
[13] G. Cormode and S. Muthukrishnan, "Estimating dominance norms on multiple data streams," in Proc. 11th Eur. Symp. Algorithms (ESA), 2003.
 
14
[14] C. R. Hadlock, Field Theory and Its Classical Problems. Washington, DC: Mathematical Assoc. America, 1978.
 
15
 
16
 
17
[17] M. Roesch, "Snort: The lightweight network intrusion detection system," , 2001 [Online]. Available: http://www. snort.org/
 
18
[18] H.Wang, D. Zhang, and K. G. Shin, "Detecting SYN flooding attacks," in Proc. IEEE INFOCOM, 2002.
 
19
20
21
 
22
[22] J. Jung, V. Paxson, A. W. Berger, and H. Balakrishnan, "Fast portscan detection using sequential hypothesis testing," in Proc. IEEE Symp. Security and Privacy, 2004.
 
23
24
 
25
 
26
[26] SPEEDRouter vl.l Product Specification. Xilinx Inc., 2001.
 
27
[27] Synlipfy Pro. Syplicity Inc. [Online]. Available: http://www.synplicity. com
 
28
[28] Dshield.org: Distributed Intrusion Detection System. SANS Inst. [On-line]. Available: http://www.dshield.org/
29
30
31
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Collaborative Colleagues:
Robert Schweller: colleagues
Zhichun Li: colleagues
Yan Chen: colleagues
Yan Gao: colleagues
Ashish Gupta: colleagues
Yin Zhang: colleagues
Peter A. Dinda: colleagues
Ming-Yang Kao: colleagues
Gokhan Memik: colleagues