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Authors: Aneri Chavda ; Katerina Potika ; Fabio Di Troia and Mark Stamp

Affiliation: Department of Computer Science, San Jose State University, San Jose, California and U.S.A.

ISBN: 978-989-758-319-3

Keyword(s): Spam, Image Spam, Support Vector Machine, SVM, Machine Learning.

Abstract: Email is one of the most common forms of digital communication. Spam is unsolicited bulk email, while image spam consists of spam text embedded inside an image. Image spam is used as a means to evade text-based spam filters, and hence image spam poses a threat to email-based communication. In this research, we analyze image spam detection using support vector machines (SVMs), which we train on a wide variety of image features. We use a linear SVM to quantify the relative importance of the features under consideration. We also develop and analyze a realistic “challenge” dataset that illustrates the limitations of current image spam detection techniques.

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Paper citation in several formats:
Chavda, A.; Potika, K.; Troia, F. and Stamp, M. (2018). Support Vector Machines for Image Spam Analysis.In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS, ISBN 978-989-758-319-3, pages 431-441. DOI: 10.5220/0006921404310441

@conference{bass18,
author={Aneri Chavda. and Katerina Potika. and Fabio Di Troia. and Mark Stamp.},
title={Support Vector Machines for Image Spam Analysis},
booktitle={Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS,},
year={2018},
pages={431-441},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006921404310441},
isbn={978-989-758-319-3},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS,
TI - Support Vector Machines for Image Spam Analysis
SN - 978-989-758-319-3
AU - Chavda, A.
AU - Potika, K.
AU - Troia, F.
AU - Stamp, M.
PY - 2018
SP - 431
EP - 441
DO - 10.5220/0006921404310441

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