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Enhancing collaborative spam detection with bloom filters

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Enhancing collaborative spam detection with bloom filters. / Yan, Jeff; Cho, Pook Leong.

Computer Security Applications Conference, 2006. ACSAC '06. 22nd Annual. IEEE, 2006. p. 414-428 4041186.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Yan, J & Cho, PL 2006, Enhancing collaborative spam detection with bloom filters. in Computer Security Applications Conference, 2006. ACSAC '06. 22nd Annual., 4041186, IEEE, pp. 414-428, 22nd Annual Computer Security Applications Conference, ACSAC 2006, Miami Beach, FL, United States, 11/12/06. https://doi.org/10.1109/ACSAC.2006.26

APA

Yan, J., & Cho, P. L. (2006). Enhancing collaborative spam detection with bloom filters. In Computer Security Applications Conference, 2006. ACSAC '06. 22nd Annual (pp. 414-428). [4041186] IEEE. https://doi.org/10.1109/ACSAC.2006.26

Vancouver

Yan J, Cho PL. Enhancing collaborative spam detection with bloom filters. In Computer Security Applications Conference, 2006. ACSAC '06. 22nd Annual. IEEE. 2006. p. 414-428. 4041186 doi: 10.1109/ACSAC.2006.26

Author

Yan, Jeff ; Cho, Pook Leong. / Enhancing collaborative spam detection with bloom filters. Computer Security Applications Conference, 2006. ACSAC '06. 22nd Annual. IEEE, 2006. pp. 414-428

Bibtex

@inproceedings{f4094c3860814ae0b5f5e4834d6a5d57,
title = "Enhancing collaborative spam detection with bloom filters",
abstract = "Signature-based collaborative spam detection (SCSD) systems provide a promising solution addressing many problems facing statistical spam filters, the most widely adopted technology for detecting junk emails. In particular, some SCSD systems can identify previously unseen spam messages as such, although intuitively this would appear to be impossible. However, the SCSD approach usually relies on huge databases of email signatures, demanding lots of resource in signature lookup, storage, transmission and merging. In this paper, we report our enhancements to two representative SCSD systems. In our enhancements, signature lookups can be performed in constant time, independent of the number of signatures in the database. Space-efficient representation can significantly reduce signature database size. A simple but fast algorithm for merging different signature databases is also supported. We use the Bloom filter technique and a novel variant of this technique to achieve all this.",
author = "Jeff Yan and Cho, {Pook Leong}",
year = "2006",
doi = "10.1109/ACSAC.2006.26",
language = "English",
isbn = "0769527167",
pages = "414--428",
booktitle = "Computer Security Applications Conference, 2006. ACSAC '06. 22nd Annual",
publisher = "IEEE",
note = "22nd Annual Computer Security Applications Conference, ACSAC 2006 ; Conference date: 11-12-2006 Through 15-12-2006",

}

RIS

TY - GEN

T1 - Enhancing collaborative spam detection with bloom filters

AU - Yan, Jeff

AU - Cho, Pook Leong

PY - 2006

Y1 - 2006

N2 - Signature-based collaborative spam detection (SCSD) systems provide a promising solution addressing many problems facing statistical spam filters, the most widely adopted technology for detecting junk emails. In particular, some SCSD systems can identify previously unseen spam messages as such, although intuitively this would appear to be impossible. However, the SCSD approach usually relies on huge databases of email signatures, demanding lots of resource in signature lookup, storage, transmission and merging. In this paper, we report our enhancements to two representative SCSD systems. In our enhancements, signature lookups can be performed in constant time, independent of the number of signatures in the database. Space-efficient representation can significantly reduce signature database size. A simple but fast algorithm for merging different signature databases is also supported. We use the Bloom filter technique and a novel variant of this technique to achieve all this.

AB - Signature-based collaborative spam detection (SCSD) systems provide a promising solution addressing many problems facing statistical spam filters, the most widely adopted technology for detecting junk emails. In particular, some SCSD systems can identify previously unseen spam messages as such, although intuitively this would appear to be impossible. However, the SCSD approach usually relies on huge databases of email signatures, demanding lots of resource in signature lookup, storage, transmission and merging. In this paper, we report our enhancements to two representative SCSD systems. In our enhancements, signature lookups can be performed in constant time, independent of the number of signatures in the database. Space-efficient representation can significantly reduce signature database size. A simple but fast algorithm for merging different signature databases is also supported. We use the Bloom filter technique and a novel variant of this technique to achieve all this.

U2 - 10.1109/ACSAC.2006.26

DO - 10.1109/ACSAC.2006.26

M3 - Conference contribution/Paper

AN - SCOPUS:39049090542

SN - 0769527167

SN - 9780769527161

SP - 414

EP - 428

BT - Computer Security Applications Conference, 2006. ACSAC '06. 22nd Annual

PB - IEEE

T2 - 22nd Annual Computer Security Applications Conference, ACSAC 2006

Y2 - 11 December 2006 through 15 December 2006

ER -