Abstract
In this paper anti-spam filtering is presented as a cumbersome service, as opposing to a software product perspective. The human effort for setting up, adaptation, maintenance and tuning of filters for spam detection is stressed. Because choosing the proper scores (relevance) for the spam filters is essential to the accuracy of the anti-spam system and one of the biggest challenges for the Apache SpamAssassin project (the most widely adopted anti-spam open-source software), we present a survey on single and multi-objective optimization studies for this purpose. Our survey constitutes a contribution and a stimulus for further research on this open research topic, with particular emphasis on evolutionary multi-objective approaches.
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References
MessageLabs Ltd., MessageLabs Intelligence, http://www.messagelabs.co.uk/intelligence.aspx
Schryen, G.: Anti-Measures: Analysis and Design. Springer, Heidelberg (2007)
Dnswl.org, http://www.dnswl.org/
SpamHaus Project Organization, The SpamHaus Project, http://www.spamhaus.org/
Geeknet Inc.: Pyzor, http://pyzor.sf.net/
Prakash, V.V.: Vipul’s Razor, http://razor.sf.net/
Sender Policy Framework (SPF) for Authorizing Use of Domains in E-Mail - version 1, http://www.ietf.org/rfc/rfc4408.txt
DomainKeys Identified Mail (DKIM) Signatures, http://www.ietf.org/rfc/rfc4871.txt
Duan, Z., Dong, Y., Gopalan, K.: DMTP: Controlling spam through message delivery differentiation. Computer Networks 51, 2616–2630 (2007)
Apache SpamAssassin Project, http://spamassassin.apache.org/
Grindstone for SPAM, http://sing.ei.uvigo.es/grindstone4spam
LingSpam dataset, http://www.aueb.gr/users/ion/data/lingspam/public.tar.gz
Spambase dataset, http://www.ics.uci.edu/~mlearn/MLRepository.html
SpamAssasin dataset, http://spamassassin.apache.org/publiccorpus/
PU1 dataset, http://www.iit.demokritos.gr/skel/i-config/downloads/pu1_encoded.tar.gz
Androutsopoulos, I., Koutsias, J., Chandrinos, K., Spyropoulos, C.: An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In: 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–167 (2000)
TREC 2005 Spam Track Public Corpus, plg.uwaterloo.ca/gvcormac/treccorpus (2005)
Findlay, D., Birk, S.: Logistic Regression and Spam Filtering. Master Thesis (2007)
Dudley, J., Barone, L., While, L.: Multi-objective spam filtering using an evolutionary algorithm Evolutionary Computation. In: IEEE World Congress on Computational Intelligence (CEC 2008), pp. 123 -130 (2008)
Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Fonseca, C., Fleming, P.: Genetic algorithms for multi-objective optimisation: formulation, discussion, and generalisation. In: Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Reading (1989)
López-Herrera, A., Herrera-Viedma, E., Herrera, F.: A Multiobjective Evolutionary Algorithm for Spam E-mail Filtering. In: 3rd International Conference on Intelligent System and Knowledge Engineering (2008)
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Yevseyeva, I., Basto-Fernandes, V., Méndez, J.R. (2011). Survey on Anti-spam Single and Multi-objective Optimization. In: Cruz-Cunha, M.M., Varajão, J., Powell, P., Martinho, R. (eds) ENTERprise Information Systems. CENTERIS 2011. Communications in Computer and Information Science, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24355-4_13
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DOI: https://doi.org/10.1007/978-3-642-24355-4_13
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