A Taxonomy of Email SPAM Filters

SPAM email is well known problem for both corporate and personal users of email. Although SPAM has been well studied, both formally and informally, SPAM continues to be a significant problem. While the various anti-SPAM techniques have been described separately, the authors are not aware of any systematic presentation of them in the literature. We address this omission by presenting a taxonomy of existing SPAM email countermeasures, and a brief description of the taxa we have proposed, and ascribe a number of existing SPAM filters to the various taxa.

[1]  Lluís Màrquez i Villodre,et al.  Boosting Trees for Anti-Spam Email Filtering , 2001, ArXiv.

[2]  Tianshun Yao,et al.  An evaluation of statistical spam filtering techniques , 2004, TALIP.

[3]  Xu Zhou,et al.  A LVQ-based neural network anti-spam email approach , 2005, OPSR.

[4]  Martin Mauve,et al.  Filtering Spam Email Based on Retry Patterns , 2007, 2007 IEEE International Conference on Communications.

[5]  Paul Gardner-Stephen,et al.  A Biologically Inspired Method of SPAM Detection , 2009, 2009 20th International Workshop on Database and Expert Systems Application.

[6]  P. Oscar Boykin,et al.  Personal Email Networks: An Effective Anti-Spam Tool , 2004, ArXiv.

[7]  Georgios Paliouras,et al.  A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists , 2004, Information Retrieval.

[8]  Shari Lawrence Pfleeger,et al.  Canning SPAM: Proposed solutions to unwanted email , 2005, IEEE Security & Privacy Magazine.

[9]  Alaa El-Halees.  Filtering spam e-mail from mixed arabic and english messages: a comparison of machine learning techniques , 2009, Int. Arab J. Inf. Technol..

[10]  Constantine D. Spyropoulos,et al.  An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages , 2000, SIGIR '00.

[11]  Simon Heron.  Spam Detection: Technologies for spam detection , 2009 .

[12]  Lu Xianliang,et al.  A LVQ-based neural network anti-spam email approach , 2005 .

[13]  Michele Colajanni,et al.  HoneySpam: Honeypots Fighting Spam at the Source , 2005, SRUTI.

[14]  Vidyasagar Potdar,et al.  Evaluation of spam detection and prevention frameworks for email and image spam: a state of art , 2008, iiWAS.

[15]  Katsuyuki Yamazaki,et al.  Density-based spam detector , 2004, IEICE Trans. Inf. Syst..

[16]  Ernesto Damiani,et al.  An Open Digest-based Technique for Spam Detection , 2004, PDCS.

[17]  Abduelbaset Goweder,et al.  AN ANTI-SPAM SYSTEM USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS , 2008 .

[18]  Joel Scanlan,et al.  Catching spam before it arrives: domain specific dynamic blacklists , 2006, ACSW.

[19]  Ahmed Khorsi,et al.  An Overview of Content-Based Spam Filtering Techniques , 2007, Informatica.

[20]  Carl Vogel,et al.  Spam filters: bayes vs. chi-squared; letters vs. words , 2003, ISICT.

[21]  Jaap-Henk Hoepman,et al.  Spam Filter Analysis , 2004, SEC.

[22]  James A. Hendler,et al.  Reputation Network Analysis for Email Filtering , 2004, CEAS.

[23]  Andrea Seraghiti,et al.  Filtering Obfuscated Email Spam by means of Phonetic String Matching , 2006, ECIR.

[24]  Carla E. Brodley,et al.  Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers , 2006, TREC.

[25]  Susan T. Dumais,et al.  A Bayesian Approach to Filtering Junk E-Mail , 1998, AAAI 1998.

[26]  Dit-Yan Yeung,et al.  A learning approach to spam detection based on social networks , 2007 .

[27]  Enrico Blanzieri,et al.  A survey of learning-based techniques of email spam filtering , 2008, Artificial Intelligence Review.

[28]  Eric Allman.  Spam, Spam, Spam, Spam, Spam, the FTC, and Spam , 2003, ACM Queue.