Skip to main content
SpringerLink
Log in

A Novel Wolf Based Trust Accumulation Approach for Preventing the Malicious Activities in Mobile Ad Hoc Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Mobile Ad hoc Network is self-organized and movable in nature, it is widely used in various applications including military and private sectors. However, security is one of the key concerns in routing because of the moving nodes; thus it is usually affected by Black Hole and Grey Hole attack. These types of malicious activities are more harmful to the network channel, and once the attack is happened it is difficult to predict and mitigate. To end this problem the current research proposed a novel Grey Wolf Trust Accumulation (GWTA) Schema in wireless mesh network architecture, thus the attacks are identified by the finest function of the GWTA model. Moreover, the predicted attacked nodes are replaced to the last position of the network medium to prevent the packet loss. Furthermore, the comparison studies proved the effectiveness of the proposed model by attaining less packet drop and high throughput ratio rate.

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
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. Computers or any other electronic devices.

References

  1. Wu, W. C. (2020). Spectrum sensing in optical wireless communication network. Transactions on Emerging Telecommunications Technologies,1, e3716.

    Google Scholar 

  2. Sultanuddin, S. J., & Ali Hussain, M. (2020). Token system-based efficient route optimization in mobile ad hoc network for v ehicular ad hoc network in smart city. Transactions on Emerging Telecommunications Technologies,1, e3853.

    Google Scholar 

  3. Ur Rahman, H., et al. (2020). A community-based social P2P network for sharing human life digital memories. Transactions on Emerging Telecommunications Technologies,1, e3866.

    Google Scholar 

  4. Sundar, R., & Kathirvel, A. (2020). Aggressively delivered mechanism over variable length density using a simulated annealing algorithm in mobile ad hoc network. Transactions on Emerging Telecommunications Technologies,1, e3863.

    Google Scholar 

  5. Bany Taha, M., et al. (2020). TD-PSO: Task distribution approach based on particle swarm optimization for vehicular ad hoc network. Transactions on Emerging Telecommunications Technologies,1, e3860.

    Google Scholar 

  6. Robinson, Y. H., et al. (2019). FD-AOMDV: fault-tolerant disjoint ad-hoc on-demand multipath distance vector routing algorithm in mobile ad-hoc networks. Journal of Ambient Intelligence and Humanized Computing,10(11), 4455–4472.

    Google Scholar 

  7. Liu, S., et al. (2019). Dynamic analysis for the average shortest path length of mobile ad hoc networks under random failure scenarios. IEEE Access,7, 21343–21358.

    Google Scholar 

  8. Madhja, A., Nikoletseas, S., & Voudouris, A. A. (2019). Adaptive wireless power transfer in mobile ad hoc networks. Computer Networks,152, 87–97.

    Google Scholar 

  9. Rath, M., et al. (2019). QTM: A QoS task monitoring system for mobile ad hoc networks. Recent Findings in Intelligent Computing Techniques (pp. 543–550). Singapore: Springer.

    Google Scholar 

  10. Saini, T. K., & Sharma, S. C. (2019). Prominent unicast routing protocols for Mobile Ad hoc Networks: Criterion, classification, and key attributes. Ad Hoc Networks,89, 58–77.

    Google Scholar 

  11. Wang, W., et al. (2019). A realistic mobility model with irregular obstacle constraints for mobile ad hoc networks. Wireless Networks,25(2), 487–506.

    Google Scholar 

  12. Elmahdi, E., Yoo, S. M., & Sharshembiev, K. (2020). Secure and reliable data forwarding using homomorphic encryption against blackhole attacks in mobile ad hoc networks. Journal of Information Security and Applications,51, 102425.

    Google Scholar 

  13. Darwish, S. M., Elmasry, A., & Ibrahim, S. H. (2019). Optimal Shortest Path in Mobile Ad-Hoc Network Based on Fruit Fly Optimization Algorithm. International Conference on Advanced Machine Learning Technologies and Applications,1, 1.

    Google Scholar 

  14. Banerjee, A., Dutta, P., & Sufian, A. (2019). Movement guided management of topology (MGMT) with balanced load in mobile ad hoc networks. International Journal of Information Technology,11(1), 149–158.

    Google Scholar 

  15. Jamali, M. A. J. (2019). A multipath QoS multicast routing protocol based on link stability and route reliability in mobile ad-hoc networks. Journal of Ambient Intelligence and Humanized Computing,10(1), 107–123.

    MathSciNet  Google Scholar 

  16. Sufian, A., Banerjee, A., & Dutta, P. (2019). Energy and velocity based tree multicast routing in mobile Ad-Hoc networks. Wireless Personal Communications,107(4), 2191–2209.

    Google Scholar 

  17. Sharma, V. K., Verma, L. P., & Kumar, M. (2019). CL-ADSP: Cross-layer adaptive data scheduling policy in mobile ad hoc networks. Future Generation Computer Systems,97, 530–563.

    Google Scholar 

  18. Lavanya, P., Reddy, V. S. K., & Prasad, A. M. (2019). Simulation and QoS metrics comparison of routing protocols for mobile ad hoc networks using network simulator (pp. 567–575)., Microelectronics, Electromagnetics and Telecommunications Singapore: Springer.

    Google Scholar 

  19. Palani, U., Suresh, K. C., & Nachiappan, A. (2019). Mobility prediction in mobile ad hoc networks using eye of coverage approach. Cluster Computing,22(6), 14991–14998.

    Google Scholar 

  20. Zeng, B., Yao, L., & Wang, R. (2019). A reliable routing decision algorithm with minimal multicast power consumption in mobile wireless network. In IEEE 7th international conference on computer science and network technology (ICCSNT). IEEE.

  21. Farheen, N. S. S., & Jain, A. (2020). Improved routing in MANET with optimized multi path routing fine-tuned with hybrid modeling. Journal of King Saud University-Computer and Information Sciences,1, 1.

    Google Scholar 

  22. Kumar, H., et al. (2020). Study and design of route repairing mechanism in MANET. Design frameworks for wireless networks (pp. 123–149). Singapore: Springer.

    Google Scholar 

  23. Juneja, K. (2020). Random-session and K-neighbour based suspected node analysis approach for cooperative blackhole detection in MANET. Wireless Personal Communications,110(1), 45–68.

    Google Scholar 

  24. Islabudeen, M., & Devi, M. K. K. (2020). A smart approach for intrusion detection and prevention system in mobile ad hoc networks against security attacks. Wireless Personal Communications,1, 1–32.

    Google Scholar 

  25. Gurung, S., & Chauhan, S. (2018). A novel approach for mitigating gray hole attack in MANET. Wireless Networks,24(2), 565–579. https://doi.org/10.1007/s11276-016-1353-5.

    Article  Google Scholar 

  26. Khamayseh, Y. M., Aljawarneh, S. A., & Asaad, A. E. (2018). Ensuring survivability against Black Hole Attacks in MANETS for preserving energy efficiency. Sustainable Computing: Informatics and Systems,18, 90–100. https://doi.org/10.1016/j.suscom.2017.07.001.

    Article  Google Scholar 

  27. Kshirsagar, V. H., Kanthe, A. M., & Simunic, D. (2018). Trust based detection and elimination of packet drop attack in the mobile ad-hoc networks. Wireless Personal Communications,100(2), 311–320. https://doi.org/10.1007/s11277-017-5070-x.

    Article  Google Scholar 

  28. Bhuvaneswari, R., & Ramachandran, R. (2018). Denial of service attack solution in OLSR based manet by varying number of fictitious nodes. Cluster Computing. https://doi.org/10.1007/s10586-018-1723-0.

    Article  Google Scholar 

  29. Jamal, T., & Butt, S. A. (2019). Malicious node analysis in MANETS. International Journal of Information Technology,11(4), 859–867. https://doi.org/10.1007/s41870-018-0168-2.

    Article  Google Scholar 

  30. Srikaanth, P. B., & Nagarajan, V. (2019). Semi-Markov chain-based grey prediction-based mitigation scheme for vampire attacks in MANETs. Cluster Computing,22(6), 15541–15549. https://doi.org/10.1007/s10586-018-2698-6.

    Article  Google Scholar 

  31. Zareie, A., Sheikhahmadi, A., & Jalili, M. (2020). Identification of influential users in social network using gray wolf optimization algorithm. Expert Systems with Applications,142, 112971.

    Google Scholar 

  32. Ali Zardari, Z., et al. (2019). A dual attack detection technique to identify black and gray hole attacks using an intrusion detection system and a connected dominating set in MANETs. Future Internet,11(3), 61.

    Google Scholar 

  33. Pal, A., et al. (2020). An efficient load balanced stable multi-path routing for mobile ad-hoc network. Microsystem Technologies,1, 1–15.

    Google Scholar 

  34. Vigenesh, M., & Santhosh, R. (2019). An efficient stream region sink position analysis model for routing attack detection in mobile ad hoc networks. Computers & Electrical Engineering,74, 273–280.

    Google Scholar 

  35. Vimala, S., Khanaa, V., & Nalini, C. (2019). A study on supervised machine learning algorithm to improvise intrusion detection systems for mobile ad hoc networks. Cluster Computing,22(2), 4065–4074.

    Google Scholar 

  36. Bala, K., Jothi, S., & Chandrasekar, A. (2019). An enhanced intrusion detection system for mobile ad-hoc network based on traffic analysis. Cluster Computing,22(6), 15205–15212.

    Google Scholar 

  37. Moudni, H., et al. (2019). Black hole attack detection using fuzzy based intrusion detection systems in MANET. Procedia Computer Science,151, 1176–1181.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering, Presidency University, Itgalpur, Rajanakunte, Yelahanka, Bangalore, 560064, India

    Ramesh Vatambeti

Authors
  1. Ramesh Vatambeti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramesh Vatambeti.

Ethics declarations

Conflict of interest

The authors declare that they have no potential conflict of interest.

Human and Animal Rights

All applicable institutional and/or national guidelines for the care and use of animals were followed.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vatambeti, R. A Novel Wolf Based Trust Accumulation Approach for Preventing the Malicious Activities in Mobile Ad Hoc Network. Wireless Pers Commun 113, 2141–2166 (2020). https://doi.org/10.1007/s11277-020-07316-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07316-z

Keywords

Search

Navigation