Improved Negative Selection Algorithm with Application to Email Spam Detection

  • Ismaila Idris
  • Ali Selamat
Part of the Studies in Computational Intelligence book series (SCI, volume 551)


Email spam is an unsolicited message sent into the mail box; it requires the need of an adaptive spam detection model to eliminate these unwanted messages. The random detector generation in negative selection algorithm (NSA) is improved upon by the implementation of particle swarm optimization (PSO) to generate detectors. In the process of detector generation, a fitness function that calculates the reach-ability distance between the non-spam space and the candidate detector, and use the result of the reach-ability distance to calculate the local density factor among the candidate detector was also implemented. The result of the experiment shows an enhancement of the improved NSA over the standard NSA.


Negative selection algorithm Particle swarm optimization Local outlier factor Spam Non-spam 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, H., Gao, X., Huang, X., Song, Z.: PSO-Optimized Negative Selection Algorithm for Anomaly Detection. In: Applications of Soft Computing, vol. 52, pp. 13–21. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Gao, X.Z., Ovaska, S.J., Wang, X.: Particle Swarm Optimization of detectors in Negative Selection Algorithm. In: 2007 ISIC IEEE International Conference on Systems, Man and Cybernetics, October 7-10, pp. 1236–1242 (2007)Google Scholar
  3. 3.
    Sotiropoulos, D.: Artificial Immune System-based Machine Learning Methodologies. PhD thesis, University of Piraeus, Piraeus, Greece (2010)Google Scholar
  4. 4.
    Oda, T., White, T.: Developing an Immunity to Spam. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 231–242. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Oda, T., White, T.: Increasing the accuracy of a spam-detecting artificial immune system. In: The 2003 Congress on Evolutionary Computation, CEC 2003, December 8-12, vol. 391, pp. 390–396 (2003)Google Scholar
  6. 6.
    Oda, T., White, T.: Immunity from Spam: An Analysis of an Artificial Immune System for Junk Email Detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 276–289. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Mohammad, A.H., Zitar, R.A.: Application of genetic optimized artificial immune system and neural networks in spam detection. Applied Soft Computing 11(4), 3827–3845 (2011)CrossRefGoogle Scholar
  8. 8.
    Yevseyeva, I., Basto-Fernandes, V., Ruano-Ordás, D., Méndez, J.R.: Optimising anti-spam filters with evolutionary algorithms. Expert Systems with Applications 40(10), 4010–4021 (2013)CrossRefGoogle Scholar
  9. 9.
    He, W., Mi, G., Tan, Y.: Parameter Optimization of Local-Concentration Model for Spam Detection by Using Fireworks Algorithm. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013, Part I. LNCS, vol. 7928, pp. 439–450. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Balthrop, J., Forrest, S., Glickman, M.R.: Revisiting LISYS: parameters and normal behavior. In: Proceedings of the 2002 Congress on Evolutionary Computing, pp. 1045–1050 (2002)Google Scholar
  11. 11.
    Forrest, S., Perelson, A.S.: Self nonself discrimination in computer (1994)Google Scholar
  12. 12.
    Wang, C., Zhao, Y.: A new fault detection method based on artificial immune systems. Asia-Pacific Journal of Chemical Engineering 3(6), 706–711 (2008)CrossRefGoogle Scholar
  13. 13.
    Sajesh, T.A., Srinivasan, M.R.: Outlier detection for high dimensional data using the Comedian approach. Journal of Statistical Computation and Simulation 82(5), 745–757 (2011)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Hopkins, M., Reeber, E., Forman, G., Jaap, S.: Spam Base Dataset. Hewlett-Packard Labs (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Software Engineering Department, Faculty of ComputingUniversiti Tecknologi MalaysiaJohor BahruMalaysia

Personalised recommendations