Abstract
This paper proposes a novel behavior-based anti-spam technology for email service based on an artificial immune-inspired clustering algorithm. The suggested method is capable of continuously delivering the most relevant spam emails from the collection of all spam emails that are reported by the members of the network. Mail servers could implement the anti-spam technology by using the “black lists” that have been already recognized. Two main concepts are introduced, which defines the behavior-based characteristics of spam and to continuously identify the similar groups of spam when processing the spam streams. Experiment results using real-world datasets reveal that the proposed technology is reliable, efficient and scalable. Since no single technology can achieve one hundred percent spam detection with zero false positives, the proposed method may be used in conjunction with other filtering systems to minimize errors.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Androutsopoulos I, Koutsias J, Chandrinos KV, Paliouras G, Spyropoulos CD (2000) An evaluation of naive Bayesian anti-spam filtering. In: Proceedings of the workshop on machine learning in the new information age
Carreras X, Marquez L (2004) Boosting trees for anti-spam email filtering. In: Proceedings of RANLP-01, 4th international conference on recent advances in natural language processing
Dasgupta D (1999) Artificial immune systems and their applications. Springer, Berlin Heidelberg New York
Drucker H, Wu D, Vapnik VN (1999) Support vector for spam categorization. IEEE Trans Neural Netw 10(5):1048– 1054
De Castro LN, Timmis JI (2003) Artificial immune systems as a novel soft computing paradigm. Softcomputing 7:526– 544
De Castro LN, Von Zuben FJ (2000) An evolutionary immune network for data clustering. In: Proceedings of the IEEE Brazilian symposium on neural networks, pp 84–89
De Castro LN, Von Zuben FJ (2001) aiNet: an artificial immune network for data analysis. In: Chapter XII, Abbass HA, Saker RA, Newton CS (eds). Data mining: a heuristic approach. Idea Group Publishing, USA, pp 231-259
De Castro LN, Von Zuben FJ (2002) Artificial immune system: a new computational intelligence approach. Springer, Berlin Heidelberg New York
Gyongyi Z., Garcia-Molina H (2005) Spam: it’s not just for inboxes anymore. IEEE Comput 38(10):28–34
Harris E (2003) The next step in the spam control war. Greylisting. White paper, August 2003
Hinde S (2003) Spam: the evolution of a nuisance. Comput Secur 22(6): 474–478
Katirai H (1999) Filtering junk E-mail: a performance comparison between genetic programming and Naïve Bayes. MSc Thesis, University of Waterloo
Lan M, Zhou W (2005) Spam filtering based on preference ranking. In: The fifth international conference on computer and information technology, pp 223–227
Mason J (2004) The SpamAssassin homepage. http://www. Spamassassin.org/index.html
Oda T, White T (2003) In: Proceedings of the congress on evolutionary computation (CEC 2003), Canberra, 1, pp 390–396
Oda T, White T (2003) Developing an immunity to spam. Genetic and evolutionary computation—GECCO 2003. In: Genetic and evolutionary computation conference, Chicago, July 12–16, 2003, Proceedings, Part I Series: Lecture notes in computer science, vol 2723. Springer, Berlin Heidelberg New York, pp 231-242
Özgür L, Güngör T, Gürgen F (2004) Adaptive anti-spam filtering for agglutinative languages: a special case for Turkish. Pattern Recognit Lett 25(16): 1819–1831
Roman R, Zhou J, Lopez J (2006) An anti-spam scheme using pre-challenges. Comput Commun (in press) (http://dx.doi.org/10.1016/j.comcom.2005.10.037)
Sakkis G, Androutsopoulos I, Olaiouras G, Karkaletsis V, Spyropoulos CD, and Stamatopoulos P (2001) Stacking classifiers for anti-spam filtering of e-mail. In: Proceedings of conference on empirical methods in natural language processing, Carnegie Mellon University, Pittsburgh
Sakkis G, Androutsopoulos I, Paliouras G, Karkaletsis V, Spyropoulos C, Stamatopoulos P (2003) A memory-based approach to anti-Spam filtering for mailing lists. Inf Retrieval 6:49–73
Sasaki M, Shinnou H (2005) Spam detection using text clustering. In: 2005 international conference on cyberworlds, pp 316–319
Schryen G (2006) A formal approach towards the effectiveness of anti-spam procedures. In: Proceedings of the 39th annual Hawaii international conference on systems sciences, vol 6, pp 129–138
Secker A, Freitas AA, Timmis J (2003). AISEC: an artificial immune system for E-mail classification. In: Sarker R, Reynolds R, Abbass H, Kay-Chen T, McKay R, Essam D, Gedeon T (eds) Proceedings of the congress on evolutionary computation, Canberra, pp 131–139
Sophos Inc. (2004) Field guide to spam http://www.sophos.com/ Spaminfo/explained/fieldguide.html. Continuously updated. Accessed March 2, 2004
Spam Whacking Working in US (2006) Comput Fraud Secur, 2006(1):2–3
Timmis J (2000) Artificial immune systems: a novel data analysis technique inspired by the immune network theory. Ph.D. Dissertation, Department of Computer Science, University of Wales
Wu C-T, Cheng K-T, Zhu Q, Wu Y-L (2005) Using visual features for anti-spam filtering. IEEE Int Conf Image Process 3: 509–512
Zhao W, Zhang Z (2005) An email classification model based on rough set theory. In: Proceedings of the 2005 international conference on active media technology (AMT 2005), pp 403–408
Zorkadis V, Karras DA, Panayotou M (2005) Efficient information theoretic strategies for classifier combination, feature extraction and performance evaluation in improving false positives and false negatives for spam e-mail filtering. Neural Netw 18(5–6):799–807
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yue, X., Abraham, A., Chi, ZX. et al. Artificial immune system inspired behavior-based anti-spam filter. Soft Comput 11, 729–740 (2007). https://doi.org/10.1007/s00500-006-0116-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-006-0116-0