Skip to main content

Advertisement

SpringerLink
Log in

An upper and lower CUSUM for signal normalization in the dendritic cell algorithm

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Signal normalization is a part of signal formalization which is a vital data pre-processing constraint required for the functioning of the dendritic cell algorithm. In existing applications, most normalization algorithms are developed purposely for a specific application with drawing on human domain expertise and very few algorithms are designed for general problems. This makes it difficult for the inexperienced user to exploit existing approaches to another problem, particularly when the initial information about the problem is limited. Therefore, this study proposes a new signal normalization method for the dendritic cell algorithm that uses the statistical upper and lower cumulative sum so that the algorithm can be applied to general classification problems. In addition, a new method to calculate the anomaly threshold based on the average mature-contact antigen value is presented to suit the proposed algorithm. The proposed model is evaluated by applying it to eight universal classification datasets and assessing its performance according to four measurement metrics: detection rate, specificity, false alarm rate, and accuracy. Its performance is compared with that of the existing dendritic cell algorithm and four non-bio-inspired classifiers, namely, rough set, decision tree, naïve Bayes, and multilayer perceptron. The results show that the proposed model outperforms the existing model and the other classifiers as well as demonstrates a significant improvement in terms of specificity, false alarm rate, and accuracy for all datasets. This indicates that the proposed normalization approach can be applied to general classification problems and can improve detection performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Aickelin U, Cayzer S (2002) The danger theory and its application to artificial immune systems. In: 1st international conference on artificial immune systems (ICARIS-2002), Canterbury, UK, pp 141–148

  2. Matzinger P (1994) Tolerance, danger and the extended family. Annu Rev Immunol 12(1):991–1045

    Article  Google Scholar 

  3. Aickelin U, Bentley P, Cayzer S, Kim J, McLeod J (2003) Danger theory: the link between AIS and IDS? In: Timmis J, Bentley P, Hart E (eds) Artificial immune systems, vol 2787. Lecture notes in computer science. Springer, Berlin, pp 147–155

    Google Scholar 

  4. Greensmith J, Aickelin U, Cayzer S (2005) Introducing dendritic cells as a novel immune inspired algorithm for anomaly detection. In: 4tn international conference in artificial immune systems (ICARIS). Springer, pp 153–167

  5. Ou C-M (2012) Host-based intrusion detection systems adapted from agent-based artificial immune systems. Neurocomputing 88:78–86

    Article  Google Scholar 

  6. Ran B, Timmis J, Tyrrell A (2010) The diagnostic dendritic cell algorithm for robotic systems. In: 2010 IEEE Congress on evolutionary computation (CEC), 18–23 July 2010, pp 1–8

  7. Huang R, Taufik H, Nagar AK (2009) Artificial dendritic cells algorithm for online break-in fraud detection. Paper presented at the second international conference on development in eSystems engineering

  8. Mohsin MFM, Bakar AA, Hamdan AR (2014) Outbreak detection model based on danger theory. Appl Soft Comput 24:612–622. doi:10.1016/j.asoc.2014.08.030

    Article  Google Scholar 

  9. Greensmith J (2007) The dendritic cell algorithm. University of Nottingham, United Kingdom

  10. Gu F, Greensmith J, Oates R, Aickelin U (2009) PCA 4 DCA: the application of principal component analysis to the dendritic cell algorithm. In: 9th annual workshop on computational intelligence. University of Nottingham, pp 124–129

  11. Chelly Z, Elouedi Z (2013) A new data pre-processing approach for the dendritic cellalgorithm based on fuzzy rough set theory. In: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion, Amsterdam, vol 1. ACM, 2464657, pp 163–164. doi:10.1145/2464576.2464657

  12. Bendiab E, Kholladi MK (2011) Recognition of plant leaves using the dendritic cell algorithm. Int J Digit Inf Wirel Commun: IJDIWC 1(1):284–292

    Google Scholar 

  13. Gu F, Greensmith J, Aickelin U (2008) Further exploration of the dendritic cell algorithm: antigen multiplier and time windows. In: Bentley P, Lee D, Jung S (eds) Artificial immune systems, vol 5132. Lecture notes in computer science. Springer, Berlin, pp 142–153

    Google Scholar 

  14. Mokhtar M, Ran B, Timmis J, Tyrrell AM (2009) A modified dendritic cell algorithm for on-line error detection in robotic systems. In: IEEE Congress on evolutionary computation, 2009. CEC ‘09. 18–21 May 2009. pp 2055–2062

  15. Murphy PM (1997) UCI repositories of machine learning and domain theories. http://www.ics.uci.edu/~mlearn/MLRepository.html. Accessed 2 Jan 2013

  16. StatLib (2005) Statlib—datasets archive. http://lib.stat.cmu/datasets. Accessed 3 Feb 2014, 2013

  17. Greensmith J, Aickelin U, Tedesco G (2010) Information fusion for anomaly detection with the dendritic cell algorithm. Inf Fusion 11(1):21–34

    Article  Google Scholar 

  18. Jun F, Yiwen L, Chengyu T, Xiaofei X (2010) Detecting software keyloggers with dendritic cell algorithm. In: 2010 international conference on communications and mobile computing (CMC), 12–14 April 2010, pp 111–115. doi:10.1109/cmc.2010.269

  19. Chung-Ming O, Ou CR (2011) Immunity-inspired host-based intrusion detection systems. In: The fifth international conference on genetic and evolutionary computing (ICGEC), 29 Aug 2011–1 Sept 2011, pp 283–286

  20. Fang X, Liu J (2013) Input data preprocessing for bots detection using the dendritic cells algorithm. In: The 6th international congress on image and signal processing (CISP) 2013, Hangzhou, China, 16–18 Dec 2013. Hangzhou Normal University, pp 1362–1366

  21. Greensmith J, Aickelin U (2007) SYN scan detection and the DCA. In: GECCO 2007, London, UK, pp 49–56

  22. Siris VA, Papagalou F (2004) Application of anomaly detection algorithms for detecting SYN flooding attacks. In: Global telecommunications conference, 2004. GLOBECOM ‘04. IEEE, 29 Nov–3 Dec 2004, vol 2054, pp 2050–2054. doi:10.1109/glocom.2004.1378372

  23. Page ES (1954) Continuous inspection schemes. Biometrika 41(1–2):100–115

    Article  MathSciNet  MATH  Google Scholar 

  24. Montgomery DC (1985) Introduction to statistical quality control, 6th edn. Introduction to Statistical Quality Control, United State of America

    MATH  Google Scholar 

  25. Chelly Z, Elouedi Z (2013) QR-DCA: a new rough data pre-processing approach for the dendritic cell algorithm. In: Tomassini M, Antonioni A, Daolio F, Buesser P (eds) Adaptive and natural computing algorithms, vol 7824. Lecture notes in computer science. Springer, Berlin, pp 140–150

    Google Scholar 

  26. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. San Francisco, Elsevier

    MATH  Google Scholar 

  27. Øhrn A, Komorowski J (1997) ROSETTA: a rough set toolkit for analysis of data. Paper presented at the proceeding of third international joint conference on information sciences, fifth international workshop on rough sets and soft computing (RSSC’97), Durham, NC, USA, March 1–5

  28. Mohsin MFM, Hamdan AR, Bakar AA (2013) The effect of normalization for real value negative selection algorithm. In: Noah S, Abdullah A, Arshad H et al (eds) Soft computing applications and intelligent systems, vol 378. Communications in computer and information science. Springer, Berlin, pp 194–205

    Chapter  Google Scholar 

  29. Al Shalabi L, Shaaban Z (2006) Normalization as a preprocessing engine for data mining and the approach of preference matrix. In: International conference on dependability of computer systems, 2006. DepCos-RELCOMEX ‘06., 25–27 May 2006, pp 207–214

Download references

Acknowledgment

The authors acknowledge financial support from Fundamental Research Grant Scheme, Ministry of Higher Education for supporting this research project through grant FRGS/1/2014/ICT02/UKM/01/2

Author information

Authors and Affiliations

  1. Data Mining and Optimization Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Mohamad Farhan Mohamad Mohsin, Abdul Razak Hamdan & Azuraliza Abu Bakar

  2. School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, Sintok, Kedah, Malaysia

    Mohamad Farhan Mohamad Mohsin

Authors
  1. Mohamad Farhan Mohamad Mohsin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Razak Hamdan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Azuraliza Abu Bakar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamad Farhan Mohamad Mohsin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohsin, M.F.M., Hamdan, A.R. & Bakar, A.A. An upper and lower CUSUM for signal normalization in the dendritic cell algorithm. Evol. Intel. 9, 37–51 (2016). https://doi.org/10.1007/s12065-016-0136-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-016-0136-3

Keywords

Search

Navigation