A multi-layer model to detect spam email at client side

Kamini Simi Bajaj

doi:10.1007/978-3-319-59608-2_20

A multi-layer model to detect spam email at client side

Kamini Simi Bajaj

School of Computer, Data & Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

A solution to spam emails remains elusive despite over a decade long research efforts on spam filtering. Among different spam detection mechanisms that have been proposed, Naïve Bayesian Content Filtering has been very popular and has attained a reasonable level of success. SpamBayes is one such content filtering spam detection tool based on Naïve Bayesian classification using textual features. It is easy to deceive the learning techniques focusing only on textual attributes. Hence, in this paper we propose a multi-layer model that imposes, on top of SpamBayes, a second layer of non-textual filtering that exploits alternative machine learning techniques. This multi-layer model improves the accuracy of classification and eliminates the grey email into spam and ham emails. The experimental results of this model are quite encouraging.

Original language	English
Title of host publication	Security and Privacy in Communication Networks -12th International Conference, SecureComm 2016, Proceedings
Editors	Robert Deng, Vinod Yegneswaran, Jian Weng, Kui Ren
Publisher	Springer Verlag
Pages	334-349
Number of pages	16
ISBN (Print)	9783319596075
DOIs	https://doi.org/10.1007/978-3-319-59608-2_20
Publication status	Published - 2017
Event	12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016 - Guangzhou, China Duration: 10 Oct 2016 → 12 Oct 2016

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	198 LNICST
ISSN (Print)	1867-8211

Conference

Conference	12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016
Country/Territory	China
City	Guangzhou
Period	10/10/16 → 12/10/16

Bibliographical note

Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

Keywords

Client based email filtering
Content filtering
Email spam
SpamBayes
Supervised learning

Access to Document

10.1007/978-3-319-59608-2_20

Cite this

Bajaj, K. S. (2017). A multi-layer model to detect spam email at client side. In R. Deng, V. Yegneswaran, J. Weng, & K. Ren (Eds.), Security and Privacy in Communication Networks -12th International Conference, SecureComm 2016, Proceedings (pp. 334-349). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 198 LNICST). Springer Verlag. https://doi.org/10.1007/978-3-319-59608-2_20

Bajaj, Kamini Simi. / A multi-layer model to detect spam email at client side. Security and Privacy in Communication Networks -12th International Conference, SecureComm 2016, Proceedings. editor / Robert Deng ; Vinod Yegneswaran ; Jian Weng ; Kui Ren. Springer Verlag, 2017. pp. 334-349 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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abstract = "A solution to spam emails remains elusive despite over a decade long research efforts on spam filtering. Among different spam detection mechanisms that have been proposed, Na{\"i}ve Bayesian Content Filtering has been very popular and has attained a reasonable level of success. SpamBayes is one such content filtering spam detection tool based on Na{\"i}ve Bayesian classification using textual features. It is easy to deceive the learning techniques focusing only on textual attributes. Hence, in this paper we propose a multi-layer model that imposes, on top of SpamBayes, a second layer of non-textual filtering that exploits alternative machine learning techniques. This multi-layer model improves the accuracy of classification and eliminates the grey email into spam and ham emails. The experimental results of this model are quite encouraging.",

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Bajaj, KS 2017, A multi-layer model to detect spam email at client side. in R Deng, V Yegneswaran, J Weng & K Ren (eds), Security and Privacy in Communication Networks -12th International Conference, SecureComm 2016, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 198 LNICST, Springer Verlag, pp. 334-349, 12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016, Guangzhou, China, 10/10/16. https://doi.org/10.1007/978-3-319-59608-2_20

A multi-layer model to detect spam email at client side. / Bajaj, Kamini Simi.
Security and Privacy in Communication Networks -12th International Conference, SecureComm 2016, Proceedings. ed. / Robert Deng; Vinod Yegneswaran; Jian Weng; Kui Ren. Springer Verlag, 2017. p. 334-349 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 198 LNICST).

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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Bajaj KS. A multi-layer model to detect spam email at client side. In Deng R, Yegneswaran V, Weng J, Ren K, editors, Security and Privacy in Communication Networks -12th International Conference, SecureComm 2016, Proceedings. Springer Verlag. 2017. p. 334-349. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-319-59608-2_20

A multi-layer model to detect spam email at client side

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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