Cluster Computing

, Volume 22, Supplement 3, pp 7039–7053 | Cite as

Tiger hash based AdaBoost machine learning classifier for secured multicasting in mobile healthcare system

  • Rajagopal VenkatesanEmail author
  • Balakrishnan Srinivasan
  • Periyasamy Rajendiran


Secure multicast routing is an important in mobile healthcare system. Few research works have been developed to prevent malicious behaviors from disclosing integrity of data in mobile healthcare systems using machine learning technique. But, the performance of conventional machine learning technique was not effectual. In order to overcome this limitation, Tiger hashing based AdaBoost with SVM classifier (TH-ASVMC) technique is proposed. The TH-ASVMC technique is designed to improve the security of multicast routing in MANETs with higher data integrity rate and therefore reducing the time taken. The TH-ASVMC technique initially used Tiger hash function which converts the patient data to be transmitted over a wireless network into a hash value for maintaining the data integrity during the process of multicasting in mobile healthcare system. After that, TH-ASVMC technique used AdaBoost with SVM classifier to classify the nodes in mobile healthcare system as authentic or unauthentic based on measurement of trust value for securing multicast routing with minimum communication overhead. Thus, TH-ASVMC technique choose the only an authentic node for routing the hash value of patient data to multiple destination nodes in mobile healthcare system. This process results in enhanced reliability and scalability of secured multicast routing. The TH-ASVMC technique conducts the simulations works on metrics such as data integrity rate, scalability, reliability and communication overhead. The simulation results shows that the TH-ASVMC technique is able to improve the reliability and data integrity rate of multicast routing as compared to state-of-the-art works.


AdaBoost hash value Mobile healthcare system Multicasting Patient data Reliability SVM classifier Tiger hash function 


  1. 1.
    Xia, H., Jia, Z., Sha, E.H.-M.: Research of trust model based on fuzzy theory in mobile ad hoc networks. IET Inf. Secur. 8(2), 88–103 (2014)CrossRefGoogle Scholar
  2. 2.
    Abdulwahid, H., Dai, B., Huang, B., Chen, Z.: Scheduled-links multicast routing protocol in MANETs. J. Netw. Comput. Appl., 63, 56–67 (2016)CrossRefGoogle Scholar
  3. 3.
    Arthur, M.P., Kannan, K.: Cross-layer based multiclass intrusion detection system for secure multicast communication of MANET in military networks. Wirel. Netw. 22(3), 1035–1059 (2016)CrossRefGoogle Scholar
  4. 4.
    Moamen, A.A., Hamza, H.S., Saroit, I.A.: Secure multicast routing protocols in mobile ad-hoc networks. Int. J. Commun Syst 27(11), 2808–2831 (2014)Google Scholar
  5. 5.
    Madhusudhanan, B., Chitra, S., Rajan, C.: Mobility based key management technique for multicast security in mobile ad hoc networks. Sci. World J. (2015). CrossRefGoogle Scholar
  6. 6.
    Gopinatha, S., Nagarajan, N.: Energy based reliable multicast routing protocol for packet forwarding in MANET. J. Appl. Res. Technol. 13(3), 374–381 (2015)CrossRefGoogle Scholar
  7. 7.
    Singh, K., Yadav, R.S.: Dynamic security scheme for multicast source authentication. Procedia Technol 4, 515–521 (2012)CrossRefGoogle Scholar
  8. 8.
    Wang, X., Yang, J., Li, Z., Li, H.: The energy-efficient group key management protocol for strategic mobile scenario of MANETs. EURASIP J. Wirel. Commun. Netw. (2014). CrossRefGoogle Scholar
  9. 9.
    Chopra, Amit, Kumar, Rajneesh: Self-organized hash based secure multicast routing over ad hoc networks. Int. J. Adv. Comput. Sci. Appl. 7(2), 155–163 (2016)Google Scholar
  10. 10.
    Arumugam, A., Jayakumar, C.: Secure multicast tree construction using bacterial foraging optimization (BFO) for MANET. Circuits Syst. 7, 4154–4168 (2016)CrossRefGoogle Scholar
  11. 11.
    Selvi, M., Velvizhy, P., Ganapathy, S., Nehemiah, H.K., Kannan, A.: A rule based delay constrained energy efficient routing technique for wireless sensor networks. Clust. Comput. (2017). CrossRefGoogle Scholar
  12. 12.
    Yadav, A.K., Soni, S.: Secure multicast key distribution in mobile ad hoc networks. Adv. Wirel. Mobile Commun. 10(4), 781–792 (2017)Google Scholar
  13. 13.
    AlQarni, B.H., AlMogren, A.S.: Reliable and energy efficient protocol for MANET multicasting. J. Comput. Netw. Commun. 2016, 1–13 (2016). CrossRefGoogle Scholar
  14. 14.
    Singh, T., Singh, J., Sharma, S.: Energy efficient secured routing protocol for MANETs. Wirel. Netw. 23(4), 1001–1009 (2017)CrossRefGoogle Scholar
  15. 15.
    Vadivel, R., Murali Bhaskaran, V.: Energy efficient with secured reliable routing protocol (EESRRP) for mobile ad-hoc networks. Procedia Technol. 4, 703–707 (2012)CrossRefGoogle Scholar
  16. 16.
    Javad Vazifehdan, R., Prasad, V., Niemegeers, I.: Energy-efficient reliable routing considering residual energy in wireless ad hoc networks. IEEE Trans. Mobile Comput. 13(2), 434–447 (2014)CrossRefGoogle Scholar
  17. 17.
    Biradar, R.C., Manvi, S.S.: Neighbor supported reliable multipath multicast routing in MANETs. J. Netw. Comput. Appl. 35(3), 1074–1085 (2012)CrossRefGoogle Scholar
  18. 18.
    Kushwaha, D.S., Singh, V.K., Singh, S., Sharma, S.: Secure communication against malicious deeds in multicast routing in MANET (survey). Int. J. Adv. Comput. Eng. Netw. 3(3), 15–19 (2015)Google Scholar
  19. 19.
    Chhabra, A., Arora, G.: Secure routing in multicast routing protocol for MANET’s. Int. J. Innov. Eng. Technol. (IJIET) 2(3), 1–8 (2013)Google Scholar
  20. 20.
    Ellcy Priana, M.: Trust based clustering and secure authentication for multicast in ad-hoc. Int. J. Comput. Appl. 108(19), 9–10 (2014)Google Scholar
  21. 21.
    Parthiban, S., Rodrigues, P.: A hyper-geometric trust factor based markov prediction mechanism for compromised rendezvous point in MANET. Arab. J. Sci. Eng. 41(8), 3187–3199 (2016)CrossRefGoogle Scholar
  22. 22.
    Muthurajkumar, S., Ganapathy, S., Vijayalakshmi, M., Kannan, A.: An intelligent secured and energy efficient routing algorithm for MANETs. Wirel. Pers. Commun. 96(2), 1753–1769 (2017)CrossRefGoogle Scholar
  23. 23.
    Sethukkarasi, R., Ganapathy, S., Yogesh, P., Kannan, A.: An intelligent neuro fuzzy temporal knowledge representation model for mining temporal patterns. J. Intell. Fuzzy Syst. 26(3), 1167–1178 (2014)zbMATHGoogle Scholar
  24. 24.
    Ganapathy, S., Kulothungan, K., Muthurajkumar, S., Vijayalakshmi, M., Yogesh, P., Kannan, A.: Intelligent feature selection and classification techniques for intrusion detection in networks: a survey. EURASIP J. Wirel. Commun. Netw. 271(1), 1–16 (2013)Google Scholar
  25. 25.
    Ganapathy, S., Yogesh, P., Kannan, A.: Intelligent agent-based intrusion detection system using enhanced multiclass SVM. Comput. Intell. Neurosci. 2012, 1–9 (2012). CrossRefGoogle Scholar
  26. 26.
    Razzaghi, T., Roderick, O., Safro, I., Marko, N.: Multilevel weighted support vector machine for classification on healthcare data with missing values. PLoS ONE (2016). CrossRefGoogle Scholar
  27. 27.
    Rahulamathavan, Y., Phan, R.C.W., Veluru, S., Cumanan, K., Rajarajan, M.: Privacy-preserving multi-class support vector machine for outsourcing the data classification in cloud. IEEE Trans. Depend. Secure Comput. 11(5), 467–479 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Rajagopal Venkatesan
    • 1
    Email author
  • Balakrishnan Srinivasan
    • 1
  • Periyasamy Rajendiran
    • 1
  1. 1.School of ComputingSASTRA Deemed UniversityThanjavurIndia

Personalised recommendations