Skip to main content
SpringerLink
Log in

Enhancing Reliability in Mobile Ad Hoc Networks (MANETs) Through the K-AOMDV Routing Protocol to Mitigate Black Hole Attacks

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

A Mobile Ad Hoc Network (MANET) is a self-organize assemblage of mobile nodes without the use of pre-existing infrastructure. They face challenges of security, routing efficiency, and network stability due to dynamic topology and limited resources. The Black Hole Attack on MANETs is a critical concern, affecting communication reliability. This malicious activity involves a node falsely advertising the shortest route to the destination, leading data packets to be routed into a “black hole” where they are dropped and causing severe disruptions. This research focuses on the Ad Hoc On-Demand Multi-Path Distance Vector Routing (AOMDV) protocol, which is preferred for its improved efficiency compared to a single-path routing protocol in MANETs. We observe, investigate, and estimate wireless ad-hoc network route optimization by reducing packet hops between nodes. We suggested a novel strategy in this paper, the K-AOMDV protocol that uses K-means clustering to prevent routing misbehavior. The efficiency of the proposed K-AOMDV (KNN-Ad-hoc on demand multi-path distance vector) routing protocol is calculated using supervised machine learning approach to predict optimal routes with delay and attacks. By employing multiple paths and dynamic route discovery, it ensures robust data delivery even in the presence of malicious nodes. This protocol’s adaptability and multi-path nature effectively minimize the effects of Black Hole Attacks, bolstering the MANETs security. Proposed algorithm has a high accuracy rate of 0.99%, 80% true positives, and 80% recall.

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

Similar content being viewed by others

References

  1. Abdali ATAN, Muniyandi RC. Optimized model for energy-aware location aided routing protocol in MANET. Int J Appl Eng Res. 2017;12(14):4631–7.

    Google Scholar 

  2. Abdali, Naser TA, Hassan R, Muniyandi RC, Aman AHM, Nguyen QN, AlKhaleefa AS. Optimized particle swarm optimization algorithm for the realization of an enhanced energy-aware location-aided routing protocol in MANET. Information. 2020;11(11):529.

    Article  Google Scholar 

  3. Abdan M & Seno SAH. Machine learning methods for intrusive detection of wormhole attack in mobile ad hoc network (MANET). Wirel Commun Mob Comput. 2022.

  4. Ali SN, Tiwari SP. Detection of wormhole attack in vahicular ad-hoc network over real map using machine learning approach with preventive scheme. J Inf Technol Manage. 2022;14:159–79.

    Google Scholar 

  5. Alsatian A, Alauthman M, Alshdaifat E, Al-Ghuwairi AR, Ahmed A-D. Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks. J Ambient Intell Humaniz Comput. 2021. https://doi.org/10.1007/s12652-021-02963-x.

    Article  Google Scholar 

  6. Bai Y, Mai Y &Wang N. Performance comparison and evaluation of the proactive and reactive routing protocols for MANETs. Wireless Telecommunications Symposium (W.T.S.), 2017;p. 1–5. IEEE.

  7. Benatia SE, Smail O, Meftah B, Rebbah M, Cousin B. A reliable multi-path routing protocol based on link quality and stability for MANETs in urban areas. Simul Modell Pract Theory. 2021;113: 102397.

    Article  Google Scholar 

  8. Bhardwaj & Kumar A. Machine learning based power efficient optimized communication ensemble model with intelligent fog computing for W.S.N.s. 2022.

  9. Bhardwaj N, Singh R. Detection and avoidance of blackhole attack in A.O.M.D.V. protocol in MANETs. Int J Appl Innov Eng Manage. 2014;3(5):376–83.

    Google Scholar 

  10. Bhole K, Agashe S & Wadgaonkar J. How expert is EXPERT for fuzzy logic-based system. In: International proceedings on advances in soft computing, intelligent systems, and applications. Springer, Singapore, 2018; p. 29–36.

  11. Chen L, Hu B, Guan ZH, Zhao L, Xuemin Shen X. Multi-agent meta-reinforcement learning for adaptive multi-path routing optimization. IEEE Trans Neural Netw Learn Syst. 2021. https://doi.org/10.1109/TNNLS.2021.3070584.

    Article  PubMed  Google Scholar 

  12. Chettibi S, Chikhi S. Dynamic fuzzy logic and reinforcement learning for adaptive energy-efficient routing in mobile ad-hoc networks. Appl Soft Comput. 2016;38:321–8.

    Article  Google Scholar 

  13. Dugaev DA, Matveev GI, Siemens E & Shuvalov VP. Adaptive reinforcement learning-based routing protocol for wireless multi-hop networks. XIV International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering (A.P.E.I.E.), 2018; p. 209–18, IEEE.

  14. Guo W, Yan C, Lu T. Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing. Int J Distrib Sensor Netw. 2019;15(2):1550147719833541.

    Article  Google Scholar 

  15. Hossain S, Hussain MS, Ema RR, Dutta S, Sarkar S & Islam T. Detecting Black hole attack by selecting appropriate routes for authentic message passing using SHA-3 and Diffie-Hellman algorithm in A.O.D.V. and A.O.M.D.V. routing protocols in MANET. 10th International Conference on Computing, Communication and Networking Technologies (I.C.C.C.N.T.), 2019;p. 1–7.

  16. Kaushik S, Tripathi K, Gupta R & Mahajan P. Performance analysis of AODV and SAODV routing protocol using SVM against black hole attack. 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), Vol. 2, 2022; p. 455–59, IEEE.

  17. Kaushik S, Tripathi K, Gupta R & Mahajan P. Futuristic analysis of machine learning based routing protocols in wireless ad hoc networks. Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT). 2021; p. 324–329. IEEE.

  18. Khan T, Singh K, Manjul M, Ahmad MM, Zain AM, Ahmadian A. A Temperature-aware trusted routing scheme for sensor networks: security approach. Comput Electric Eng. 2022;98: 107735.

    Article  Google Scholar 

  19. Kumar A, Singh K, Khan T. L-RTAM: Logarithm based reliable trust assessment model for WBSNs. J Discrete Math Sci Cryptogr. 2021;24(6):1701–16.

    Article  Google Scholar 

  20. Kumar A, Singh K, Khan T, Ahmadian A, Md Saad MH, Manjul M. ETAS: an efficient trust assessment scheme for BANs. IEEE Access. 2021;9:83214–33.

    Article  Google Scholar 

  21. Mili R & Chikhi S. Reinforcement learning based routing protocols analysis for mobile ad-hoc networks. Int Conf Mach Learn Netw. 2018; 247–256.

  22. Mirza S, Gujarathi T and Bhole K. Cardiovascular risk assessment using intuitionistic fuzzy logic system. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (I.C.C.C.N.T.), p. 1–7. IEEE

  23. Murty K, Rajalakshmi MVDS. Secure and light weight Aodv (SLW-AODV) routing protocol for resilience against blackhole attack in manets. Int J Soft Comput Eng (IJSCE). 2023;13(1). (ISSN: 2231–2307 (Online))

  24. Rad D, Rad G, Maier R, Demeter E, Dicu A, Popa M, Alexuta D, Floroian D, Mărineanu VD. A fuzzy logic modeling approach on psychological data. J Intell Fuzzy Syst Preprint. 2022. https://doi.org/10.3233/JIFS-219274.

    Article  Google Scholar 

  25. Raj JS. Machine learning-based resourceful clustering with load optimization for wireless sensor networks. J Ubiquitous Comput Commun Technol (UCCT). 2020;2(01):29–38.

    Google Scholar 

  26. Reddy B, Prabhakar & Bhaskar. The AODV routing protocol with built-in security to counter blackhole attack in MANET, 2nd International Conference on Functional Material, Manufacturing and Performances. 2021.

  27. Safaei B, Monazzah AMH, Bafroei MB & Ejlali A. Reliability side-effects in the Internet of Things application layer protocols. In: 22nd International Conference on System Reliability and Safety (I.C.S.R.S.), 2017; p. 207–12.

  28. Sarao P. Evaluation of traffic models under multiple black hole attack in wireless mesh network. J Commun. 2023;18(3).

  29. Sivanesan N & Archana KS. A machine learning approach to detect network layer attacks in mobile ad hoc networks. Int J Early Childhood. 2022;14(03).

  30. Srinidhi N, Nagarjun NE, Kumar SMD. Hybrid algorithm for efficient node and path in opportunistic IoT network. J Inf Technol Manage. 2021;13:68–91.

    Google Scholar 

  31. Tami A, Hacene SB, Cherif MA. Detection and prevention of blackhole attack in the AOMDV routing protocol. J Commun Softw Syst. 2021;17(1):1–12.

    Article  Google Scholar 

  32. Tian Y & Hou R. An improved A.O.M.D.V. routing protocol for the internet of things. In 2010 International Conference on Computational Intelligence and Software Engineering, 2010;p. 1–4. IEEE.

  33. Verma C, Gupta CC. Epidemiological model of stability analysis of wireless sensor network under malware attack. J Inf Technol Manag. 2022;14:69–88.

    Google Scholar 

  34. Yasin A, Zant MA. Detecting and isolating blackhole attacks in MANET using timer based baited technique. Wirel Commun Mob Comput. 2018. https://doi.org/10.1155/2018/9812135.

    Article  Google Scholar 

Download references

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. Amity School of Engineering and Technology, Amity University Haryana, Gurugram, 122413, India

    Sheetal Kaushik

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Haryana, Gurugram, 122413, India

    Khushboo Tripathi & Rashmi Gupta

  3. IITM Janakpuri, New Delhi, India

    Prerna Mahajan

Authors
  1. Sheetal Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khushboo Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rashmi Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prerna Mahajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sheetal Kaushik.

Ethics declarations

Conflict of Interest

The authors declare no potential conflict of interest regarding the publication of this work. In addition, the ethical issues, including plagiarism, informed consent, misconduct, data fabrication and, or falsification, double publication and, or submission, and redundancy, have been completely witnessed by the authors.

Additional information

Publisher's Note

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

This article is part of the topical collection “Security for Communication and Computing Application” guest edited by Karan Singh, Ali Ahmadian, Ahmed Mohamed Aziz Ismail, R S Yadav, Md. Akbar Hossain, D. K. Lobiyal, Mohamed Abdel-Basset, Soheil Salahshour, Anura P. Jayasumana, Satya P. Singh, Walid Osamy, Mehdi Salimi and Norazak Senu.

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

Kaushik, S., Tripathi, K., Gupta, R. et al. Enhancing Reliability in Mobile Ad Hoc Networks (MANETs) Through the K-AOMDV Routing Protocol to Mitigate Black Hole Attacks. SN COMPUT. SCI. 5, 263 (2024). https://doi.org/10.1007/s42979-023-02585-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02585-4

Keywords

Search

Navigation