Comparative Analysis of Danger Theory Variants in Measuring Risk Level for Text Spam Messages

  • Kamahazira Zainal
  • Mohd Zalisham Jali
  • Abu Bakar Hasan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 753)


The issue of spam has been uprising since decades ago. Impact loss from various aspects has attacked the daily life most of us. Many approaches such as policy and guidelines establishment, rules and regulations enforcement, and even anti-spam tools installation appeared to be not enough to restrain the problem. To make things even worse, the spam’s recipients still easily get enticed and lured with the spam content. Hence, an advanced medium that acts as an implicit decision maker is desperately required to assist users to obstruct their eagerness responding against spam. The simulation of spam risk assessment in this paper is purposely to give some insights of how users can identify the imminent danger of received text spam. It is demonstrated by predicting the potential hazard with three different levels of risk (high, medium and low), according to its possible impact loss. A series of simulation has been conducted to visualize this concept using Danger Theory variants of Artificial Immune Systems (AIS), namely Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). The corpus of messages from UCI Machine Learning Repository has been deployed to illustrate the analysis. The outcome of these simulations verified that dDCA has consistently outperformed DCA in precisely assessing the risk level with severity concentration value for text spam messages. The findings of this work has demonstrated the feasibility of immune theory in risk measurement that eventually assisting users in their decision making.


Dendritic Cell Algorithm (DCA) Deterministic Dendritic Cell Algorithm (dDCA) Risk classification Signals processing Text mining Text spam risk assessment 



This research is fully funded by the Ministry of Higher Education of Malaysia and Research Management Centre of USIM via grant research with code USIM/FRGS/FST/32/50315.


  1. 1.
    Bujang, Y.R., Hussin, H.: Should we be concerned with spam emails ? A look at its impacts and implications. International Islamic University MalaysiaGoogle Scholar
  2. 2.
    Theoharidou, M., Mylonas, A., Gritzalis, D.: A Risk Assessment Method for Smartphones (2016)Google Scholar
  3. 3.
    Zhang, Y., Xiao, Y., Ghaboosi, K., Zhang, J., Deng, H.: A survey of cyber crimes. Secur. Commun. Netw. 5, 422–437 (2011)CrossRefGoogle Scholar
  4. 4.
    de Natris, W.: Best Practice Forum on Regulation and Mitigation of Unsolicited Communications (2014)Google Scholar
  5. 5.
    Yeboah-Boateng, E.O., Amanor, P.M.: Phishing, SMiShing & Vishing: an assessment of threats against mobile devices. J. Emerg. Trends Comput. Inf. Sci. 5(4), 297–307 (2014)Google Scholar
  6. 6.
    Zainal, K., Jali, M.Z.: A perception model of spam risk assessment inspired by danger theory of artificial immune systems. In: International Conference on Computer Science and Computational Intelligence (ICCSCI), vol. 59, pp. 152–161 (2015)Google Scholar
  7. 7.
    Timmis, J., Knight, T., de Castro, L.N., Hart, E.: An Overview of Artificial Immune Systems (2002)Google Scholar
  8. 8.
    Liu, F., Wang, Q., Gao, X.: Survey of Artificial Immune System, pp. 985–989 (2005)Google Scholar
  9. 9.
    Dasgupta, D.: Advances in artificial immune systems. IEEE Comput. Intell. Mag. 1(4), 40–49 (2006)CrossRefGoogle Scholar
  10. 10.
    Hart, E., Timmis, J.: Application areas of AIS: the past, the present and the future. Appl. Soft Comput. J. 8(1), 191–201 (2008)CrossRefGoogle Scholar
  11. 11.
    Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: models and applications. Appl. Soft Comput. J. 11(2), 1574–1587 (2011)CrossRefGoogle Scholar
  12. 12.
    Read, M., Andrews, P., Timmis, J.: Artificial Immune Systems (2008)Google Scholar
  13. 13.
    Matzinger, P.: Tolerance, danger and the extended family. Annu. Rev. Immunol. 12, 991–1045 (1994)CrossRefGoogle Scholar
  14. 14.
    Greensmith, J.: The Dendritic Cell Algorithm. University of Nottingham (2007)Google Scholar
  15. 15.
    Greensmith, J., Aickelin, U.: The Deterministic Dendritic Cell Algorithm (2008)Google Scholar
  16. 16.
    Brownlee, J.: Dendritic cell algorithm. In: Clever Algorithms: Nature Inspired Programming Recipes. Creative Commons, pp. 312–318 (2011)Google Scholar
  17. 17.
    Greensmith, J., Aickelin, U.: Artificial dendritic cells: multi-faceted perspectives. In: Human-Centric Information Processing Through Granular Modelling, vol. 182, pp. 375–395 (2009)Google Scholar
  18. 18.
    Greensmith, J., Aickelin, U., Cayzer, S.: Detecting Danger : The Dendritic Cell Algorithm (2010)Google Scholar
  19. 19.
    Aickelin, U., Greensmith, J.: Sensing danger: innate immunology for intrusion detection. Inf. Secur. Tech. Rep. 12(4), 218–227 (2007)CrossRefGoogle Scholar
  20. 20.
    Greensmith, J., Aickelin, U., Twycross, J.: Articulation and clarification of the dendritic cell algorithm dendritic cells (2009)Google Scholar
  21. 21.
    Gu, F., Greensmith, J., Aickelin, U.: Further exploration of the dendritic cell algorithm: antigen multiplier and time windows. In: 7th International Conference Artificial Immune System, pp. 142–153 (2008)Google Scholar
  22. 22.
    Musselle, C.J.: Insights into the Antigen Sampling Component of the Dendritic Cell Algorithm (2010)Google Scholar
  23. 23.
    Zainal, K., Jali, M.Z.: A review of feature extraction optimization in SMS spam messages classification. In: International Conference on Soft Computing in Data Science (SCDS), vol. 545, pp. 158–170 (2016)Google Scholar
  24. 24.
    Zainal, K., Jali, M.Z.: The design and development of spam risk assessment prototype. in silico of danger theory variants. Int. J. Adv. Comput. Sci. Appl. 8(4), 401–410 (2017)Google Scholar
  25. 25.
    Zainal, K., Jali, M.Z.: The significant effect of feature selection methods in spam risk assessment using dendritic cell algorithm. In: International Conference on Information and Communication Technology (ICoICT 2017), pp. 277–284 (2017)Google Scholar
  26. 26.
    Sethi, G., Bhootna, V.: SMS spam filtering application using Android. Int. J. Comput. Sci. Inf. Technol. 5(3), 4624–4626 (2014)Google Scholar
  27. 27.
    Zhang, H., Wang, W.: Application of Bayesian method to spam SMS filtering. In: IEEE, pp. 1–3 (2009)Google Scholar
  28. 28.
    Uysal, K., Gunal, S., Ergin, S., Gunal, E.S.: The impact of feature extraction and selection on SMS spam filtering. IEEE 19(5), 67–72 (2013)Google Scholar
  29. 29.
    Uysal, A.K., Gunal, S., Ergin, S., Gunal, E.S.: A novel framework for SMS spam filtering. IEEE (2012)Google Scholar
  30. 30.
    Almeida, T.A., Hidalgo, J.M.G.: UCI machine learning repository (2012). Accessed 27 Mar 2014
  31. 31.
    Gu, F., Greensmith, J., Aickelin, U.: Theoretical formulation and analysis of the deterministic dendritic cell algorithm. BioSystems 111(2), 127–135 (2013)CrossRefGoogle Scholar
  32. 32.
    Gu, F., Greensmith, J., Aicklein, U.: The dendritic cell algorithm for intrusion detection. In: Biologically Inspired Networking and Sensing: Algorithms and Architectures, Bio-Inspired Communication and Networking, IGI Global, pp. 84–102, January 2011Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Science and TechnologyUniversiti Sains Islam Malaysia (USIM)NilaiMalaysia
  2. 2.Faculty of Engineering and Built EnvironmentUniversiti Sains Islam Malaysia (USIM)NilaiMalaysia

Personalised recommendations