Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Systems Design and Applications

ISDA 2018 2018: Intelligent Systems Design and Applications pp 853–863Cite as

An Efficient Outlier Detection Mechanism for RFID-Sensor Integrated MANET

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 940))

Abstract

The proposed outlier detection scheme is multi-filtered, outlier stage outlier detection approach for resource constrained devices and networks. Multi-filtered consists of detection using trust, indices and performance based outliers whereas multi-stage detection detect outliers at MAC, routing and application layer. A novel trust management mechanism is proposed for trust based outlier detection followed by internal and external indices for second stage filtering and outlier detection. Thereafter, performance of individual and group nodes is measured for outlier detection and subsequent processing. Simulation results shows cluster stability with different number of clusters. Maximum stability for 0 to 500 nodes (small scale network) is observed with 25 clusters, 500 to 3000 nodes (medium scale network) is observed with 39 clusters and 3000 to 5000 nodes (large scale network) is observed with 52 clusters. The proposed approach shows cluster stability of 61% (approax.).

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Cluster Validation Statistics: Must Know Methods - Articles - STHDA. http://www.sthda.com/english/articles/29-cluster-validation-essentials/97-cluster-validation-statistics-must-know-methods/. Accessed 05 Jul 2018

  2. Brock, G., Pihur, V., Datta, S., Datta, S.: clValid : an R package for cluster validation. J. Stat. Softw. 25(4), 1–22 (2008)

    Article  Google Scholar 

  3. Charrad, M., Ghazzali, N., Boiteau, B., Niknafs, A.: NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61(6), 1–8 (2014)

    Article  Google Scholar 

  4. Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, Cambridge (2009)

    Google Scholar 

  5. Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  Google Scholar 

  6. Dunn, J.C.: Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4(1), 95–104 (1974)

    Article  MathSciNet  Google Scholar 

  7. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. (1979)

    Google Scholar 

  8. Caliñski, T., Harabasz, J.: A dendrite method foe cluster analysis. Commun. Stat. 3, 1–27 (1974)

    Google Scholar 

  9. Moulavi, D., Jaskowiak, P.A., Campello, R.J.G.B., Zimek, A., Sander, J.: Density-based clustering validation. In: Proceedings of the 2014 SIAM International Conference on Data Mining (2014)

    Google Scholar 

  10. Evaluation of clustering. https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html#fig:clustfg3. Accessed 05 Jul 2018

  11. Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J., Wu, S.: Understanding and enhancement of internal clustering validation measures. IEEE Trans. Cybern. 43, 982–994 (2013)

    Article  Google Scholar 

  12. Kovács, F., Legány, C., Babos, A.: Cluster validity measurement techniques. In: 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, pp. 388–394 (2006)

    Google Scholar 

  13. Huang, S., Cheng, Y., Lang, D., Chi, R., Liu, G.: A formal algorithm for verifying the validity of clustering results based on model checking. PLoS One 9(3), e90109 (2014)

    Article  Google Scholar 

  14. Gurung, S., Chauhan, S.: A dynamic threshold based approach for mitigating black-hole attack in MANET. Wirel. Netw. 1–15 (2017)

    Google Scholar 

  15. The Network Simulator - ns-2. https://www.isi.edu/nsnam/ns/. Accessed 05 Jul 2018

  16. Van Craenendonck, T., Leuven, K., Blockeel, H.: Using internal validity measures to compare clustering algorithms. In: ICML, pp. 1–8 (2015)

    Google Scholar 

  17. Panos, C., Xenakis, C., Stavrakakis, I.: A novel intrusion detection system for MANETs. In: Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), pp. 1–10 (2010)

    Google Scholar 

  18. Feng, F., Liu, X., Yong, B., Zhou, R., Zhou, Q.: Anomaly detection in ad-hoc networks based on deep learning model: a plug and play device. Ad Hoc Netw. 1–9 (2018)

    Google Scholar 

  19. Rafsanjani, M.K., Movaghar, A., Koroupi, F.: Investigating intrusion detection systems in MANET and comparing IDSs for detecting misbehaving nodes. World Acad. Sci. Eng. Technol. 44(1), 351–355 (2008)

    Google Scholar 

  20. Li, Y. and Wei, J.: Guidelines on selecting intrusion detection methods in MANET. In: Proceedings of the Information Systems Educators Conference, pp. 10:30–10:55 (2004)

    Google Scholar 

  21. Anantvalee, T., Wu, J.: A survey on intrusion detection in mobile ad hoc networks. In: Wireless Network Security, pp. 159–180 (2007)

    Google Scholar 

  22. Narayandas, V., Tiruvayipati, S., Hanmandlu, M., Thimmareddy, L.: Anomaly detection system in a cluster based MANET. In: Computer Communication, Networking and Internet Security, pp. 11–21 (2017)

    Google Scholar 

  23. Sánchez-Casado, L., Maciá-Fernández, G., García-Teodoro, P., Magán-Carrión, R.: A model of data forwarding in MANETs for lightweight detection of malicious packet dropping. Comput. Netw. 87(1), 44–58 (2015)

    Article  Google Scholar 

  24. Kumar, A., Gopal, K., Aggarwal, A.: Novel trust hierarchical construction for RFID sensor-Based MANETs using ECCs. ETRI J. 37(1), 186–196 (2015)

    Article  Google Scholar 

  25. Kumar, A., Gopal, K., Aggarwal, A.: A novel lightweight key management scheme for RFID-sensor integrated hierarchical MANET based on internet of things. Int. J. Adv. Intell. Paradig. 9(2–3), 220–245 (2017)

    Google Scholar 

Download references

Acknowledgement

The authors would like to thank SEED Division of University of Petroleum and Energy Studies. This work is part of research project (https://sites.google.com/view/adarshproject2/) sponsored by University of Petroleum and Energy Studies (UPES), Dehradun, India under Grant: UPES/R&D/180918/14.

Author information

Authors and Affiliations

  1. University of Petroleum and Energy Studies, Dehradun, India

    Adarsh Kumar & Alok Aggarwal

Authors
  1. Adarsh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alok Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adarsh Kumar .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumar, A., Aggarwal, A. (2020). An Efficient Outlier Detection Mechanism for RFID-Sensor Integrated MANET. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_80

Download citation

Publish with us

Policies and ethics

Search

Navigation