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Novel trust evaluation using NSGA-III based adaptive neuro-fuzzy inference system

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Abstract

Recently Mobile adhoc networks (MANETs) have received the great attention of researchers as these models provide a wide range of applications. But MANET nodes are prone to various security threats. To overcome this issue, many trust management frameworks have been implemented in the literature. It has been found that the use of machine learning can predict trust values more efficiently. However, machine learning performance suffers from the hyper-parameters tuning and over-fitting issues. Therefore, in this paper, novel trust management is proposed. initially, the Adaptive neuro-fuzzy inference system (ANFIS) is used to train the trust prediction model. Thereafter, a non-dominated sorting genetic algorithm-III (NSGA-III) is used to tune the hyper-parameters of the ANFIS model. Precision, recall, and root mean squared error metrics are used to design a multi-objective fitness function. Optimized link state routing (OLSR) protocol is used for comparative analyses purpose. Three different attacks are applied on the designed network i.e., link spoofing, jellyfish, and gray hole attacks to obtain the dataset. Comparative analysis reveals that the proposed trust evaluation model outperforms the competitive trust evaluation models in terms of various performance metrics such as routing overheads, average end to end latency, packet delivery ratio, and throughput. Thus, the proposed protocol is more secure against various security threats.

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References

  1. Bhuvaneswari, R., Ramachandran, R.: Denial of service attack solution in OLSR based manet by varying number of fictitious nodes. Clust. Comput. 22(5), 12689–12699 (2019)

    Article  Google Scholar 

  2. Vanitha, K., Rahaman, A.Z.: Preventing malicious packet dropping nodes in manet using IFHM based SAODV routing protocol. Clust. Comput. 22(6), 13453–13461 (2019)

    Article  Google Scholar 

  3. Serhani, A., Naja, N., Jamali, A.: Aq-routing: mobility-, stability-aware adaptive routing protocol for data routing in manet-IoT systems. Clust. Comput. 23(1), 13–27 (2020)

    Article  Google Scholar 

  4. Kandan, J.M., Sabari, A.: Fuzzy hierarchical ant colony optimization routing for weighted cluster in MANET. Clust. Comput. 22(4), 9637–9649 (2019)

    Google Scholar 

  5. Sangeetha, S., Dhaya, R., Kanthavel, R.: Improving performance of cooperative communication in heterogeneous manet environment. Clust. Comput. 22(5), 12389–12395 (2019)

    Article  Google Scholar 

  6. Basavegowda, H.S., Dagnew, G.: Deep learning approach for microarray cancer data classification. CAAI Trans. Intell. Technol. 5(1), 22–33 (2020)

    Article  Google Scholar 

  7. Kumaravel, A., Chandrasekaran, M.: Performance analysis of malicious node detection in manet using ANFIS classification approach. Clust. Comput. 22(6), 13445–13452 (2019)

    Article  Google Scholar 

  8. Ghosh, S., Shivakumara, P., Roy, P., Pal, U., Lu, T.: Graphology based handwritten character analysis for human behaviour identification. CAAI Trans. Intell. Technol. 5(1), 55–65 (2020)

    Article  Google Scholar 

  9. Anand, M., Sasikala, T.: Efficient energy optimization in mobile ad hoc network (MANET) using better-quality AODV protocol. Clust. Comput. 22(5), 12681–12687 (2019)

    Article  Google Scholar 

  10. Wang, R., Yu, H., Wang, G., Zhang, G., Wang, W.: Study on the dynamic and static characteristics of gas static thrust bearing with micro-hole restrictors. Int. J. Hydromechatronics 2(3), 189–202 (2019)

    Article  Google Scholar 

  11. Wiens, T.: Engine speed reduction for hydraulic machinery using predictive algorithms. Int. J. Hydromechatronics 2(1), 16–31 (2019)

    Article  Google Scholar 

  12. Osterland, S., Weber, J.: Analytical analysis of single-stage pressure relief valves. Int. J. Hydromechatronics 2(1), 32–53 (2019)

    Article  Google Scholar 

  13. Gupta, B., Tiwari, M., Lamba, S.S.: Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement. CAAI Trans. Intell. Technol. 4(2), 73–79 (2019)

    Article  Google Scholar 

  14. Xia, H., Jia, Z., Li, X., Ju, L., Sha, E.H.-M.: Trust prediction and trust-based source routing in mobile ad hoc networks. Ad Hoc Netw. 11(7), 2096–2114 (2013)

    Article  Google Scholar 

  15. Tan, S., Li, X., Dong, Q.: Trust based routing mechanism for securing OSLR-based manet. Ad Hoc Netw. 30, 84–98 (2015)

    Article  Google Scholar 

  16. Djedjig, N., Tandjaoui, D., Medjek, F., Romdhani, I.: Trust-aware and cooperative routing protocol for IoT security. J. Inf. Secur. Appl. 52, 102467 (2020)

    Google Scholar 

  17. Gong, W., You, Z., Chen, D., Zhao, X., Gu, M., Lam, K.-Y.: Trust based routing for misbehavior detection in ad hoc networks. J. Netw. 5(5), 551 (2010)

    Google Scholar 

  18. Islabudeen, M., Devi, M.K.: A smart approach for intrusion detection and prevention system in mobile ad hoc networks against security attacks. Wirel. Pers. Commun. 1–32 (2020)

  19. Khanna, N., Sachdeva, M.: Study of trust-based mechanism and its component model in manet: current research state, issues, and future recommendation. Int. J. Commun. Syst. 32(12), e4012 (2019)

    Article  Google Scholar 

  20. Gunasekaran, M., Premalatha, K.: Teap: trust-enhanced anonymous on-demand routing protocol for mobile ad hoc networks. IET Inf. Secur. 7(3), 203–211 (2013)

    Article  Google Scholar 

  21. Xia, H., Jia, Z., Ju, L., Zhu, Y.: Trust management model for mobile ad hoc network based on analytic hierarchy process and fuzzy theory. IET Wirel. Sens. Syst. 1(4), 248–266 (2011)

    Article  Google Scholar 

  22. Fatima, S.K., Fatima, S.G., Sattar, S.A., Srinivasa Rao, D.D.: An independent trust model for manet based on fuzzy logic rules. Int. J. Adv. Res. Eng. Technol. 10(2), 278–289 (2019)

    Article  Google Scholar 

  23. Xu, J., Feng, S., Liang, W., Ke, J., Meng, X., Zhang, R., Shou, D.: An algorithm for determining data forwarding strategy based on recommended trust value in manet. Int. J. Embed. Syst. 12(4), 544–553 (2020)

    Article  Google Scholar 

  24. Manoranjini, J., Chandrasekar, A., Jothi, S.: Improved QoS and avoidance of black hole attacks in manet using trust detection framework. Automatika 60(3), 274–284 (2019)

    Article  Google Scholar 

  25. Sadayan, G., Ramaiah, K.: Enhanced data security in manet using trust-based bayesian statistical model with RSSI by AOMDV. Concurr. Comput. e5397 (2019)

  26. Nguyen, D.Q., Toulgoat, M., Lamont, L.: Impact of trust-based security association and mobility on the delay metric in manet. J. Commun. Netw. 18(1), 105–111 (2016)

    Article  Google Scholar 

  27. Wadhwani, G.K., Khatri, S.K., Mutto, S.: Trust framework for attack resilience in manet using AODV. J. Discret. Math. Sci. Cryptogr. 23(1), 209–220 (2020)

    Article  Google Scholar 

  28. Josephine, J.A., Senthilkumar, S.: Tanimoto support vector regressive linear program boost based node trust evaluation for secure communication in manet. Wirel. Pers. Commun. 1–21 (2020)

  29. Marathe, N., Shinde, S.K.: ITCA, an IDS and trust solution collaborated with ack based approach to mitigate network layer attack on manet routing. Wirel. Pers. Commun. 107(1), 393–416 (2019)

    Article  Google Scholar 

  30. Merlin, R.T., Ravi, R.: Novel trust based energy aware routing mechanism for mitigation of black hole attacks in manet. Wirel. Pers. Commun. 104(4), 1599–1636 (2019)

    Article  Google Scholar 

  31. Kaur, M., Singh, D., Uppal, R.S.: Parallel strength pareto evolutionary algorithm-II based image encryption. IET Image Process. 14(6), 1015–1026 (2019)

    Article  Google Scholar 

  32. Gupta, A., Singh, D., Kaur, M.: An efficient image encryption using non-dominated sorting genetic algorithm-III based 4-d chaotic maps. J. Ambient Intell. Hum. Comput. 11(3), 1309–1324 (2020)

    Article  Google Scholar 

  33. Kaur, M., Singh, D., Kumar, V., Sun, K.: Color image dehazing using gradient channel prior and guided l0 filter. Inf. Sci. 521, 326–342 (2020)

    Article  MATH  Google Scholar 

  34. Kaur, M., Singh, D., Sun, K., Rawat, U.: Color image encryption using non-dominated sorting genetic algorithm with local chaotic search based 5d chaotic map. Future Gener. Comput. Syst. 107, 333–350 (2020)

    Article  Google Scholar 

  35. Pragathi, Y.S., Shetty, S.P.: Design and implementation of ANFIS to enhance the performance of secure lar routing protocol in manets

  36. Budyal, V., Manvi, S.S.: ANFIS and agent based bandwidth and delay aware anycast routing in mobile ad hoc networks. J. Netw. Comput. Appl. 39, 140–151 (2014)

    Article  Google Scholar 

  37. Shabut, A.M., Dahal, K.P., Bista, S.K., Awan, I.U.: Recommendation based trust model with an effective defence scheme for manets. IEEE Trans. Mob. Comput. 14(10), 2101–2115 (2014)

    Article  Google Scholar 

  38. Marimuthu, M., Krishnamurthi, I.: Enhanced OLSR for defense against DOS attack in ad hoc networks. J. Commun. Netw. 15(1), 31–37 (2013)

    Article  Google Scholar 

  39. Venkataraman, R., Pushpalatha, M., Rao, T.R.: Regression-based trust model for mobile ad hoc networks. IET Inf. Secur. 6(3), 131–140 (2012)

    Article  Google Scholar 

  40. Li, X., Jia, Z., Zhang, P., Zhang, R., Wang, H.: Trust-based on-demand multipath routing in mobile ad hoc networks. IET Inf. Secur. 4(4), 212–232 (2010)

    Article  Google Scholar 

  41. Zhang, H., Wang, X., Memarmoshrefi, P., Hogrefe, D.: A survey of ant colony optimization based routing protocols for mobile ad hoc networks. IEEE Access 5, 24139–24161 (2017)

    Article  Google Scholar 

  42. Cheng, H., Yang, S.: Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Eng. Appl. Artif. Intell. 23(5), 806–819 (2010)

    Article  MathSciNet  Google Scholar 

  43. Yang, S., Cheng, H., Wang, F.: Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Trans. Syst. Man Cybern. Part C 40(1), 52–63 (2009)

    Article  Google Scholar 

  44. Surendran, S., Prakash, S.: An ACO look-ahead approach to QOS enabled fault-tolerant routing in manets. China Commun. 12(8), 93–110 (2015)

    Article  Google Scholar 

  45. Nandi, B., Barman, S., Paul, S.: Genetic algorithm based optimization of clustering in ad hoc networks, arXiv preprint arXiv:1002.2194

  46. Ali, H., Shahzad, W., Khan, F.A.: Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization. Appl. Soft Comput. 12(7), 1913–1928 (2012)

    Article  Google Scholar 

  47. Kaliappan, M., Augustine, S., Paramasivan, B.: Enhancing energy efficiency and load balancing in mobile ad hoc network using dynamic genetic algorithms. J. Netw. Comput. Appl. 73, 35–43 (2016)

    Article  Google Scholar 

Download references

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  1. Department of Computer Science and Engineering, Punjabi University, Patiala, India

    Jasleen Kaur & Supreet Kaur

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  1. Jasleen Kaur
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Correspondence to Jasleen Kaur.

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Kaur, J., Kaur, S. Novel trust evaluation using NSGA-III based adaptive neuro-fuzzy inference system. Cluster Comput 24, 1781–1792 (2021). https://doi.org/10.1007/s10586-020-03218-8

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