Geodesic
Two-classification artificial immune system
Vestnik Samarskogo universiteta. Estestvennonaučnaâ seriâ, no. 7 (2014), pp. 207-220 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

In the article the practical aspect of application of principles of biological immune system for solving the problem of analysis and classification of email is viewed. In the capacity of analyzed emails ordinary emails (electronic mail) and mails from closed systems (electronic document flow or business management systems) were taken. In the article two-classification artificial immune system was developed with further comparison of effectiveness of their usage with naive Bayesian classification algorithm. Practical realization of the developed system with the application in the system of analysis of emails of the commercial structure is carried out.
Keywords: аrtificial immune system, clonal selection theory, electronic document flow, business management systems, emails, spam, classification of emails, affinity. 
@article{VSGU_2014_7_a19,
     author = {M. E. Burlakov},
     title = {Two-classification artificial immune system},
     journal = {Vestnik Samarskogo universiteta. Estestvennonau\v{c}na\^a seri\^a},
     pages = {207--220},
     year = {2014},
     number = {7},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VSGU_2014_7_a19/}
}
TY  - JOUR
AU  - M. E. Burlakov
TI  - Two-classification artificial immune system
JO  - Vestnik Samarskogo universiteta. Estestvennonaučnaâ seriâ
PY  - 2014
SP  - 207
EP  - 220
IS  - 7
UR  - http://geodesic.mathdoc.fr/item/VSGU_2014_7_a19/
LA  - ru
ID  - VSGU_2014_7_a19
ER  - 
%0 Journal Article
%A M. E. Burlakov
%T Two-classification artificial immune system
%J Vestnik Samarskogo universiteta. Estestvennonaučnaâ seriâ
%D 2014
%P 207-220
%N 7
%U http://geodesic.mathdoc.fr/item/VSGU_2014_7_a19/
%G ru
%F VSGU_2014_7_a19
M. E. Burlakov. Two-classification artificial immune system. Vestnik Samarskogo universiteta. Estestvennonaučnaâ seriâ, no. 7 (2014), pp. 207-220. http://geodesic.mathdoc.fr/item/VSGU_2014_7_a19/

[1] Puniškis D., Laurutis R., Dirmeikis R., “An Artificial Neural Nets for Spam e-mail Recognition”, Electronics and electrical engineering, 5, Cambridge, 2006

[2] Burnet F. M., The Clonal Selection Theory of Acquired Immunity, Cambridge University Press, Cambridge, 1959, 312 pp.

[3] Castro L., Timmis J., “An Artificial Immune Network for Multimodal Function Optimization”, Proceedings of IEEE Congress on Evolutionary Computation, CEC'02, v. 1, 2002, 699–674

[4] Somayaji A., Hofmeyr S., Forrest S., “Principles of a Computer Immune System”, Proceedings of the Second New Security Paradigms Workshop (1997), 75–82

[5] Christodorescu M., Jha S., Kruegel Ch., “Mining specifications of malicious behavior”, Proceedings of the the 6th joint meeting of the European software engineering on The foundations of software engineering (2007), 5–14

[6] Ismaila I., Ali S., “A Spam Detection Model Based on Negative Selection Algorithm”, International Journal on Data Mining and Intelligent Information Technology Applications, 2 (2011), 15–17

[7] Repository of machine learning databases, (accessed: 02.05.2014) http://archive.ics.uci.edu/ml/machine-learning-databases/README

[8] de Castro Leandro N., Timmis J., Artificial Immune Systems: A New Computational Intelligence Approach, Springer, 2004, 273 pp.

[9] “An artificial immune system architecture for computer security applications”, IEEE Transactions on Evolutionary Computation (accessed: 03.05.2014) http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.9398

[10] A Plan for Spam, (accessed: 05.05.2014) http://www.paulgraham.com/spam.html