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Efficient sequence alignment of network traffic

Authors:

Christian Kreibich,

Jon CrowcroftAuthors Info & Claims

IMC '06: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement

Pages 307 - 312

https://doi.org/10.1145/1177080.1177121

Published: 25 October 2006 Publication History

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Abstract

String comparison algorithms, inspired by methods used in bioinformatics, have recently gained popularity in network applications. In this paper we demonstrate the need for careful selection of alignment models if such algorithms are to yield the desired results when applied to network traffic. We introduce a novel variant of the Jacobson-Vo algorithm employing a flexible gap-minimising alignment model suitable for network traffic, and find that our software implementation outperforms the commonly used Smith-Waterman approach by a factor of 33 on average and up to 58.5 in the best case on a wide range of network protocols.

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Index Terms

Efficient sequence alignment of network traffic
1. Networks
  1. Network protocols

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Published In

IMC '06: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement

October 2006

356 pages

ISBN:1595935614

DOI:10.1145/1177080

General Chairs:
Jussara Almeida
Federal University of Minas Gerais, Brazil
,
Virgilio Almeida
Federal University of Minas Gerais, Brazil
,
Program Chair:
Paul Barford
University of Wisconsin -- Madison, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2006

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IMC06: Internet Measurement Conference

October 25 - 27, 2006

Rio de Janeriro, Brazil

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Shu XZhang JDaphne Yao DFeng W(2016)Fast Detection of Transformed Data LeaksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2015.250327111:3(528-542)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1109/TIFS.2015.2503271
Huang SZhang JLu SHua XHauptmann ANgo CXue XJiang YSnoek CVasconcelos N(2015)Social Friend Recommendation Based on Network Correlation and Feature Co-ClusteringProceedings of the 5th ACM on International Conference on Multimedia Retrieval10.1145/2671188.2749325(315-322)Online publication date: 22-Jun-2015
https://dl.acm.org/doi/10.1145/2671188.2749325
Shu XZhang JYao DFeng W(2015)Rapid and parallel content screening for detecting transformed data exposure2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFCOMW.2015.7179383(191-196)Online publication date: Apr-2015
https://doi.org/10.1109/INFCOMW.2015.7179383
Sorokin I(2011)Comparing files using structural entropyJournal in Computer Virology10.1007/s11416-011-0153-97:4(259-265)Online publication date: 1-Nov-2011
https://dl.acm.org/doi/10.1007/s11416-011-0153-9
Jung JSheth AGreenstein BWetherall DMaganis GKohno TNing PSyverson PJha S(2008)Privacy oracleProceedings of the 15th ACM conference on Computer and communications security10.1145/1455770.1455806(279-288)Online publication date: 27-Oct-2008
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Fabjański KKruk T(2008)Network Traffic Classification by Common Subsequence FindingProceedings of the 8th international conference on Computational Science, Part I10.1007/978-3-540-69384-0_55(499-508)Online publication date: 23-Jun-2008
https://dl.acm.org/doi/10.1007/978-3-540-69384-0_55
Coull SCollins MWright CMonrose FReiter M(2007)On web browsing privacy in anonymized NetFlowsProceedings of 16th USENIX Security Symposium on USENIX Security Symposium10.5555/1362903.1362926(1-14)Online publication date: 6-Aug-2007
https://dl.acm.org/doi/10.5555/1362903.1362926

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Multiple sequence alignment using a GLOCSA guided genetic algorithm

Pairwise sequence alignment algorithms: a survey

Space Efficient Diagonal Linear Space Sequence Alignment

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