SpringerLink
Log in

Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Cooperation among mobile nodes during the routing process is indispensable for attaining reliable data delivery between the source and destination nodes in the Mobile ad hoc networks (MANETs). This cooperation between mobile nodes sustains the performance of the network especially when they are been deployed for handling an emergency scenario like forest fire, flooding, and military vehicle monitoring. In specific, the criteria considered for determining the cooperation degree of mobile nodes attributed towards the routing proves is dynamic and uncertain. In this paper, Hybrid Bayesian, and Modified Grey PROMETHEE-AL Model-based Trust Estimation (MGPALTE) technique is proposed for thwarting Malicious and Selfish Nodes for enforcing cooperation between the mobile nodes in MANETs. It specifically utilized Bayesian Best–Worst Method method for generating the set of weights related to objective group criteria. It is also used for aggregating the judgements of cooperation determined during indirect monitoring process. Moreover, Grey theory is integrated with the classical PROMETHEE for improving its efficacy in terms of accuracy with respect to ranking of mobile nodes participating in the process of routing. This proposed MGPALTE technique isolated the malicious mobile nodes from the routing path depending on the threshold of detection. The simulation results of the proposed MGPALTE technique confirmed better packer delivery rate of 19.21%, improved throughput of 22.38%, minimized delay of 23.19%, and reduced end-to-end delay of 21.36%, better than the competitive cooperation enforcement strategies with different number of mobile nodes in the network.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Availability of data and material

Data sharing not applicable—no new data generated.

Code availability

Custom code.

References

  1. Akhtar, M. A., & Sahoo, G. (2021). Enhancing cooperation in MANET using neighborhood compressive sensing model. Egyptian Informatics Journal, 22(3), 373–387.

    Article  Google Scholar 

  2. Thiagarajan, R., Ganesan, R., Anbarasu, V., Baskar, M., Arthi, K., & Ramkumar, J. (2021). Optimised with secure approach in detecting and isolation of malicious nodes in MANET. Wireless Personal Communications, 119, 21–35.

    Article  Google Scholar 

  3. Christopher Paul, A., Bhanu, D., Dhanapal, R., & Jebakumar Immanuel, D. (2022, February). An efficient authentication using monitoring scheme for node misbehaviour detection in MANET. In: International Conference on Computing, Communication, Electrical and Biomedical Systems (pp. 627–633). Cham: Springer International Publishing.

  4. Usha, M. S., & Ravishankar, K. C. (2021). Implementation of trust-based novel approach for security enhancements in MANETs. SN Computer Science, 2(4), 257.

    Article  Google Scholar 

  5. Nandi, M., & Anusha, K. (2021). An optimized and hybrid energy aware routing model for effective detection of flooding attacks in a manet environment. Wireless Personal Communications, pp. 1–19.

  6. Gopala Krishnan, C., Nishan, A. H., Gomathi, S., & Aravind Swaminathan, G. (2022). Energy and trust management framework for MANET using clustering algorithm. Wireless Personal Communications, 122(2), 1267–1281.

    Article  Google Scholar 

  7. Janakiraman, S., & Jayasingh, B. B. (2019). A hyper-exponential factor-based Semi-Markov prediction mechanism for selfish rendezvous nodes in MANETs. Wireless Personal Communications, 108(3), 1493–1511.

  8. Vijayalakshmi, S., Bose, S., Logeswari, G., & Anitha, T. (2023). Hybrid defense mechanism against malicious packet drop** attack for MANET using game theory. Cyber security and applications, 1, 100011.

    Article  Google Scholar 

  9. Allimuthu, U., & Mahalakshmi, K. (2022). Intelligent route discovery towards rushing attacks in ad hoc wireless networks. Journal of Ambient Intelligence and Humanized Computing, 13(2), 921–960.

    Article  Google Scholar 

  10. Arulkumaran, G., & Gnanamurthy, R. K. (2019). Fuzzy trust approach for detecting black hole attack in mobile adhoc network. Mobile Networks and Applications, 24, 386–393.

    Article  Google Scholar 

  11. Alnumay, W., Ghosh, U., & Chatterjee, P. (2019). A trust-based predictive model for mobile ad hoc network in Internet of things. Sensors, 19(6), 1467.

    Article  Google Scholar 

  12. Udhaya Sankar, S. M., Jagadish Kumar, N., Elangovan, G., & Praveen, R. (2023). An integrated Z-number and dematel-based cooperation enforcement scheme for thwarting malicious nodes in MANETs. Wireless Personal Communications, 130(4), 2531–2563.

    Article  Google Scholar 

  13. Ourouss, K., Naja, N., & Jamali, A. (2021). Defending against smart grayhole attack within MANETs: A reputation-based ant colony optimization approach for secure route discovery in DSR protocol. Wireless Personal Communications, 116, 207–226.

    Article  Google Scholar 

  14. Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96, 102075.

    Article  Google Scholar 

  15. Subburayalu, G., Duraivelu, H., Raveendran, A. P., Arunachalam, R., Kongara, D., & Thangavel, C. (2021). Cluster based malicious node detection system for mobile Ad-Hoc network using ANFIS classifier. Journal of Applied Security Research, pp. 1–19.

  16. Farahani, G. (2021). Black hole attack detection using K-nearest neighbor algorithm and reputation calculation in mobile ad hoc networks. Security and Communication Networks, 2021.

  17. Rani, P., Verma, S., Kaur, N., Wozniak, M., Shafi, J., & Ijaz, M. F. (2021). Robust and secure data transmission using artificial intelligence techniques in ad-hoc networks. Sensors, 22(1), 251.

    Article  Google Scholar 

  18. Durga Devi, S., & Rukmani Devi, D. (2021). Malicious node and malicious observer node detection system in MANETs. Concurrency and Computation: Practice and Experience, 33(3), e5241.

    Article  Google Scholar 

  19. Mylsamy, R., & Jayaprakash, K. (2022). Fuzzy preference ranking organization method for enrichment evaluation‐based node cooperation technique for improving QoS in MANETs. International Journal of Communication Systems, e5168.

  20. Yamini, K. A. P., Stephy, J., Suthendran, K., & Ravi, V. (2022). Improving routing disruption attack detection in MANETs using efficient trust establishment. Transactions on Emerging Telecommunications Technologies, 33(5), e4446.

    Article  Google Scholar 

  21. Fayaz, M., Mehmood, G., Khan, A., Abbas, S., Fayaz, M., & Gwak, J. (2022). Counteracting selfish nodes using reputation-based system in mobile Ad Hoc networks. Electronics, 11(2), 185.

    Article  Google Scholar 

  22. Jagatheswari, S., Ramalingam, P., & Chandra Priya, J. (2022). Improved grey relational analysis-based TOPSIS method for cooperation enforcing scheme to guarantee quality of service in MANETs. International Journal of Information Technology, 14(2), 887–897.

    Article  Google Scholar 

  23. Bounouni, M., Bouallouche-Medjkoune, L., Beraza, A., & Daoud, A. (2022). Eliminating selective drop** attack in mobile Ad Hoc network. Wireless Personal Communications, 123(4), 3291–3308.

    Article  Google Scholar 

  24. Lakshmi, G. V., & Vaishnavi, P. (2023). A trusted security approach to detect and isolate routing attacks in mobile ad hoc networks. Journal of Engineering Research.

  25. Janakiraman, S., & Priya, M. D. (2022). Selfish node detection scheme based on bates distribution inspired trust factor for MANETs. EAI Endorsed Transactions on Energy Web, 9(6), e1–e1.

    Article  Google Scholar 

  26. Abdulzahra, A. M. K., & Al-Qurabat, A. K. M. (2022). A clustering approach based on fuzzy C-means in wireless sensor networks for IoT applications. Karbala International Journal of Modern Science, 8(4), 579–595.

    Article  Google Scholar 

  27. Abdulzahra, A. M. K., Al-Qurabat, A. K. M., & Abdulzahra, S. A. (2023). Optimizing energy consumption in WSN-based IoT using unequal clustering and sleep scheduling methods. Internet of Things, 22, 100765.

    Article  Google Scholar 

  28. Al-Qurabat, A. K. M., Mohammed, Z. A., & Hussein, Z. J. (2021). Data traffic management based on compression and MDL techniques for smart agriculture in IoT. Wireless Personal Communications, 120(3), 2227–2258.

    Article  Google Scholar 

  29. Al-Qurabat, M., & Kadhum, A. (2021). A lightweight Huffman-based differential encoding lossless compression technique in IoT for smart agriculture. International Journal of Computing and Digital System.

  30. Saeedi, I. D. I., & Al-Qurabat, A. K. M. (2022). Perceptually important points-based data aggregation method for wireless sensor networks. Baghdad Science Journal, 19(4), 0875–0875.

    Article  Google Scholar 

  31. Al-Qurabat, A. K. M., & Abdulzahra, S. A. (2020, November). An overview of periodic wireless sensor networks to the internet of things. In: IOP Conference Series: Materials Science and Engineering (Vol. 928, No. 3, p. 032055). IOP Publishing.

  32. Al-Qurabat, A. K. M., Salman, H. M., & Finjan, A. A. R. (2022). Important extrema points extraction-based data aggregation approach for elongating the WSN lifetime. International Journal of Computer Applications in Technology, 68(4), 357–368.

    Article  Google Scholar 

  33. Saeedi, I. D. I., & Al-Qurabat, A. K. M. (2022). An energy-saving data aggregation method for wireless sensor networks based on the extraction of extrema points. In: AIP Conference Proceedings (Vol. 2398, No. 1). AIP Publishing.

  34. Nedham, W. B., & Al-Qurabat, A. K. M. (2022). An improved energy efficient clustering protocol for wireless sensor networks. In: 2022 International Conference for Natural and Applied Sciences (ICNAS) (pp. 23–28). IEEE.

  35. Mylsamy, R., & Jayaprakash, K. (2023). Z number improved reference ideal method-based decision-making process for enforcing cooperation during data dissemination in mobile ad hoc networks. International Journal of Communication Systems, 36(13), e5522.

    Article  Google Scholar 

Download references

Funding

There is no funding received for this research work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, CARE College of Engineering, Trichy, Tamil Nadu, 620009, India

    J. Suresh

  2. Department of Electronics and Communication Engineering, Sri Indu College of Engineering and Technology, Hyderabad, TS, 501510, India

    J. Martin Sahayaraj

  3. Department of Artificial Intelligence, Saveetha School of Engineering, Thandalam, Chennai, 602 105, India

    B. Rajakumar

  4. Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bangalore, India

    N. Jayapandian

Authors
  1. J. Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Martin Sahayaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. Rajakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. Jayapandian
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JS and MS formulated the problem, implemented, conducted the literature review and written the introduction part, BR and NJ performed the experimental validation process and reviewed the complete manuscript.

Corresponding author

Correspondence to J. Suresh.

Ethics declarations

Conflict of interest

The authors declare that there is no competing interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suresh, J., Sahayaraj, J.M., Rajakumar, B. et al. Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs. Wireless Netw 30, 1697–1718 (2024). https://doi.org/10.1007/s11276-023-03605-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03605-0

Keywords

Search

Navigation