Shyamily P. V.; Anoop B. K.

A Centralized Federated Framework for Black-Hole Attack Detection in Mobile Ad Hoc Net-works using Gini Canberra Spearman-Based K-Means Clustering and Judy-Verkle Tree Verification

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Author(s)

1. Department of Artificial Intelligence and Data Science, Sree Narayana Guru College of Engineering and Technology, Kannur, India

2. Kerala State Electronics Development Cooperation, Kerala, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijwmt.2026.04.21

Received: 28 May 2026 / Revised: 17 Jun. 2026 / Accepted: 25 Jun. 2026 / Published: 8 Aug. 2026

Index Terms

Linear Scaling based Shark Smell Optimization algorithm (LS-SSOA), Round Log sum of BCrypt based Message Authentication Code (RLBC-MAC), Gini Canberra Spearman based K-Means Algorithm (GCS-KMA), Judy Verkle Tree (JVT), Black-hole attack, Ad-hoc OnDemand D

Abstract

Black-hole (BH) attacks are among the most critical security threats in Mobile Ad Hoc Networks (MANETs) due to their decentralized and highly dynamic nature. Existing protection mechanisms primarily rely on threshold-based monitoring, trust evaluation, and route observation techniques, which often suffer from limited packet information, poor scalability, and inadequate attack verification capabilities. To address these limitations, this paper proposes a centralized federated framework for black-hole attack detection in MANETs. The proposed framework integrates Linear Scaling-based Shark Smell Optimization Algorithm (LS-SSOA) for Cluster Head (CH) selection, Gini Canberra Spearman-based K-Means Algorithm (GCS-KMA) for clustering, Round Log Sum BCrypt-based Message Authentication Code (RLBC-MAC) for secure route authorization, and Judy-Verkle Tree (JVT) for malicious path verification. A federated learning architecture consisting of a global server and local models is employed to continuously monitor network activities and improve attack detection efficiency. Experimental evaluation was conducted using MANET networks comprising 50–250 nodes in a Python-based simulation environment. The proposed framework achieved a detection accuracy of 98.96%, a false prediction rate of 2.06%, throughput of 6445 kbps, and reduced computational overhead compared with existing methods. The results demonstrate that the proposed centralized federated framework significantly improves network security, route reliability, and attack detection performance in MANET environments.

Cite This Paper

Shyamily P. V., Anoop B. K., "A Centralized Federated Framework for Black-Hole Attack Detection in Mobile Ad Hoc Networks using Gini Canberra Spearman-Based K-Means Clustering and Judy-Verkle Tree Verification", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.4, pp. 363-377, 2026. DOI:10.5815/ijwmt.2026.04.21

Reference

[1]M. Al-Shabi, “Performance Ameliorations of AODV by Black Hole Attack Detection Utilizing IDSAODV as Well as Reverse AODV,” International Journal of Communication Networks and Information Security (IJCNIS), vol. 14, no. 2, pp. 99–110, Aug. 2022, doi: https://doi.org/10.17762/ijcnis.v14i2.5483.
[2]S. A. Arunmozhi, S. Rajeswari, and Y. Venkataramani, “Swarm Intelligence Based Routing with Black Hole Attack Detection in MANET,” Computer Systems Science and Engineering, vol. 44, no. 3, pp. 2337–2347, 2023, doi: https://doi.org/10.32604/csse.2023.024340.
[3]M. Boopathi and R. Seetha, “Accurate Detection of Multi-layer Packet Dropping Attacks Using Distributed Mobile Agents in MANET,” Journal of Physics: Conference Series, vol. 1979, no. 1, p. 012040, Aug. 2021, doi: https://doi.org/10.1088/1742-6596/1979/1/012040.
[4]M. Bounouni, L. Bouallouche-Medjkoune, A. Beraza, and A. Daoud, “Eliminating Selective Dropping Attack in Mobile Ad Hoc Network,” Wireless Personal Communications, vol. 123, no. 4, pp. 3291–3308, Nov. 2021, doi: https://doi.org/10.1007/s11277-021-09289-z.
[5]G. Farahani, “Black Hole Attack Detection Using K-Nearest Neighbor Algorithm and Reputation Calculation in Mobile Ad Hoc Networks,” Security and Communication Networks, vol. 2021, pp. 1–15, Aug. 2021, doi: https://doi.org/10.1155/2021/8814141.
[6]Mustafa Maad Hamdi et al., “A study review on Gray and Black Hole in Mobile Ad Hoc Networks (MANETs),” 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Jun. 2022, doi: https://doi.org/10.1109/hora55278.2022.9800011.
[7]D. Kancharakuntla and H. El-Ocla, “EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks,” Electronics, vol. 11, no. 21, p. 3480, Oct. 2022, doi: https://doi.org/10.3390/electronics11213480.
[8]R. Khalladi, M. Rebbah, and O. Smail, “A new efficient approach for detecting single and multiple black hole attacks,” The Journal of Supercomputing, vol. 77, no. 7, pp. 7718–7736, Jan. 2021, doi: https://doi.org/10.1007/s11227-020-03596-1.
[9]R. Makani and B. V. R. Reddy, “Trust-based-tuning of Bayesian-watchdog intrusion detection for fast and improved detection of black hole attacks in mobile ad hoc networks,” International Journal of Advanced Intelligence Paradigms, vol. 21, no. 1/2, p. 53, 2022, doi: https://doi.org/10.1504/ijaip.2022.121029.
[10]V. Mankotia, R. K. Sunkaria, and S. Gurung, “DT-AODV: a dynamic threshold protocol against black-hole attack in MANET,” Sādhanā, vol. 48, no. 4, Sep. 2023, doi: https://doi.org/10.1007/s12046-023-02227-8.
[11]Idriss Moumen, Najat Rafalia, Jaafar Abouchabaka, and Youssef Chatoui, “AODV-based Defense Mechanism for Mitigating Blackhole Attacks in MANET,” E3S Web of Conferences, vol. 412, pp. 01094–01094, Jan. 2023, doi: https://doi.org/10.1051/e3sconf/202341201094.
[12]R. Ramesh et al., “A Low-Latency and High-Throughput Multipath Technique to Overcome Black Hole Attack in Mobile Ad Hoc Network (MTBD),” Security and Communication Networks, vol. 2022, pp. 1–13, Aug. 2022, doi: https://doi.org/10.1155/2022/8067447.
[13]B.Prabhakar Reddy, B. Bhaskar Reddy and B. Dhanajaya, “The AODV routing protocol with built-in security to counter blackhole attack in MANET,” Materials Today: Proceedings, Aug. 2021, doi: https://doi.org/10.1016/j.matpr.2021.08.039.
[14]S. Shafi, S. Mounika, and S. Velliangiri, “Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET,” Procedia Computer Science, vol. 218, pp. 2309–2318, 2023, doi: https://doi.org/10.1016/j.procs.2023.01.206.
[15]S. Sivanesh and V. R. S. Dhulipala, “Accurate and Cognitive Intrusion Detection System (ACIDS): a Novel Black Hole Detection Mechanism in Mobile Ad Hoc Networks,” Mobile Networks and Applications, vol. 26, no. 4, pp. 1696–1704, Jan. 2020, doi: https://doi.org/10.1007/s11036-019-01505-2.
[16]U. Srilakshmi, S. A. Alghamdi, V. A. Vuyyuru, N. Veeraiah, and Y. Alotaibi, “A Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks,” IEEE Access, vol. 10, pp. 14260–14269, 2022, doi: https://doi.org/10.1109/access.2022.3144679.
[17]U. Srilakshmi, N. Veeraiah, Y. Alotaibi, S. A. Alghamdi, O. I. Khalaf, and B. V. Subbayamma, “An Improved Hybrid Secure Multipath Routing Protocol for MANET,” IEEE Access, vol. 9, pp. 163043–163053, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3133882.
[18]M. Thebiga and R. SujiPramila, “A New Mathematical and Correlation Coefficient Based Approach to Recognize and to Obstruct the Black Hole Attacks in Manets Using DSR Routing,” Wireless Personal Communications, vol. 114, no. 2, pp. 975–993, Apr. 2020, doi: https://doi.org/10.1007/s11277-020-07403-1.
[19]N. Veeraiah et al., “Trust aware secure energy efficient hybrid Protocol for MANET,” IEEE Access, pp. 1–1, 2021, doi: https://doi.org/10.1109/access.2021.3108807.
[20]S.Vijayalakshmi and T. Anitha, “Hybrid Defense Mechanism Against Malicious Packet Dropping Attack for Manet Using Game Theory,” SSRN Electronic Journal, 2022, doi: https://doi.org/10.2139/ssrn.4205757

International Journal of Wireless and Microwave Technologies (IJWMT)