J. Jabez; N. Jayanthi; Elangovan Muniyandy; R. Mohanapriya

Blockchain-enhanced Detection of Malicious Nodes in WSNs Using Parallel Triple Graph Attention-based Convolution Network

PDF (1101KB), PP.210-229

Views: 0 Downloads: 0

Author(s)

J. Jabez ^1,* N. Jayanthi ² Elangovan Muniyandy ³ R. Mohanapriya ⁴

1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology Chennai, Tamil Nadu, India

2. Department of Information Technology, JJ College of Engineering and Technology, Trichy, Tamil Nadu, India

3. Department of Biosciences, Saveetha School of Engineering.Saveetha Institute of Medical and Technical Sciences, Chennai - 602 105, Tamil Nadu, India

4. Department of Electronics and Communication Engineering, Paavai Engineering College, Paavai Nagar, NH-44, Pachal -637 018.Namakkal, Tamil Nadu, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2026.02.12

Received: 19 Nov. 2025 / Revised: 8 Jan. 2026 / Accepted: 3 Feb. 2026 / Published: 8 Apr. 2026

Index Terms

Blockchain, InterPlanetary File System, Intrusion Detection System, Malicious Node, Wireless Sensor Networks, WSN-BFSF, WSN-DS

Abstract

A Wireless Sensor Network (WSN) is an efficient system for monitoring distributed areas and controlling environments; however, such networks are susceptible to malicious node attacks that bring forth network insecurity and untrustworthy data. WSNs are vulnerable to malicious nodes and cyber attackers that can interfere with data transmission, leading to compromised decision-making systems. Traditional security techniques against WSNs lack flexibility in real-time detection and data integrity because of constrained processing resources and vulnerabilities from centralized storage. This work aims to improve detection accuracy through a multi-stage strategy, which constitutes the general objective of this research. The presented model uses WSN-DS and WSN-BFSF datasets. The data are pre-processed using Localized-Global Depth Normalization for uniformity, followed by feature selection via Boosted Tern-Cat Hunting Optimization, which combines Cat Hunting Optimization and Boosted Sooty Tern techniques to reduce dimensionality. The attack detection is performed by a Parallel Triple Graph Attention-based Convolution Network, which employs Quantum Parallel Deep Convolution and Triple Graph Attention Networks. The RMRO optimizes the model's parameters to classify more accurately, and the benign data are safely stored through the Consensus-Aided PoA Decision Blockchain Engine and InterPlanetary File System. This approach achieved 99.4% accuracy, 99.3% recall, and 99.5% F1 score on the WSN-DS dataset and 99.2% accuracy, 99.1% precision, and 99.3% F1 score on the WSN-BFSF dataset while showing robustness across different combinations of sensors. Hence, the Tri-QPdCNet offers a pioneering approach toward securing WSNs from dynamic and persistent attacks by providing an improved framework for anomaly detection using a strong, scalable architecture, augmented with blockchain technology. That leads to more robust WSN infrastructures that can be more securely and smoothly deployed in real-time critical environments.

Cite This Paper

J. Jabez, N. Jayanthi, Elangovan Muniyandy, R. Mohanapriya, "Blockchain-enhanced Detection of Malicious Nodes in WSNs Using Parallel Triple Graph Attention-based Convolution Network", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.2, pp.210-229, 2026. DOI:10.5815/ijcnis.2026.02.12

Reference

[1]A. K. S. Yadav, S. S. Sivaraju, B. Radha, M. Sushith, S. Srithar, and M. Kanchana, “Malicious node detection using SVM and secured data storage using blockchain in WSN,” Int. J. Syst. Assur. Eng. Manag., vol. •••, pp. 1–11, 2024. DOI: 10.1007/s13198-024-02564-9
[2]G. G. Gebremariam, J. Panda, and S. Indu, “Blockchain‐Based Secure Localization against Malicious Nodes in IoT‐Based Wireless Sensor Networks Using Federated Learning,” Wirel. Commun. Mob. Comput., vol. 2023, no. 1, p. 8068038, 2023. DOI: 10.1155/2023/8068038
[3]X. Yang, Y. Chen, X. Qian, T. Li, and X. Lv, “BCEAD: A Blockchain‐Empowered Ensemble Anomaly Detection for Wireless Sensor Network via Isolation Forest,” Secur. Commun. Netw., vol. 2021, no. 1, p. 9430132, 2021. DOI: 10.1155/2021/9430132
[4]M. A. Almaiah, “A new scheme for detecting malicious attacks in wireless sensor networks based on blockchain technology,” in Artificial intelligence and blockchain for future cybersecurity applications. Cham: Springer International Publishing, 2021, pp. 217–234. DOI: 10.1007/978-3-030-74575-2_12
[5]L. K. Ramasamy, F. K. KP, A. L. Imoize, J. O. Ogbebor, S. Kadry, and S. Rho, “Blockchain-based wireless sensor networks for malicious node detection: A survey,” IEEE Access, vol. 9, pp.128765–128785, 2021. DOI: 10.1109/ACCESS.2021.3111923
[6]A. Rehman, S. Abdullah, M. Fatima, M. W. Iqbal, K. A. Almarhabi, M. U. Ashraf, et al., “Ensuring security and energy efficiency of wireless sensor network by using blockchain,” Appl. Sci. (Basel), vol. 12, no. 21, p. 10794, 2022. DOI: 10.3390/app122110794
[7]R. R. Chandan, A. Balobaid, N. L. S. Cherukupalli, G. H L, F. Flammini, and R. Natarajan, “Secure modern wireless communication network based on blockchain technology,” Electronics (Basel), vol. 12, no. 5, 1095, 2023.
[8]S. J. Hsiao and W. T. Sung, “Employing blockchain technology to strengthen security of wireless sensor networks,” IEEE Access, vol. 9, pp. 72326–72341, 2021.
[9]O. A. Khashan, “Blockchain-machine learning fusion for enhanced malicious node detection in wireless sensor networks,” Knowl. Base. Syst., vol. 304, p. 112557, 2024. DOI: 10.1016/j.knosys.2024.112557
[10]A. U. Khan, M. B. E. Sajid, A. Rauf, M. N. Saqib, F. Zaman, and N. Javaid, “Exploiting Blockchain and RMCV‐Based Malicious Node Detection in ETD‐LEACH for Wireless Sensor Networks,” Wirel. Commun. Mob. Comput., vol. 2022, no. 1, p. 7281872, 2022. DOI: 10.1155/2022/7281872
[11]A. Saeed, M. U. Javed, A. Almogren, N. Javaid, and M. Jamil, “Employing blockchain and IPFS in WSNs for malicious node detection and efficient data storage,” Wirel. Netw., vol. 30, no. 4, pp. 1–16, 2024. DOI: 10.1007/s11276-023-03648-3
[12]S. Awan, N. Javaid, S. Ullah, A. U. Khan, A. M. Qamar, and J. G. Choi, “Blockchain based secure routing and trust management in wireless sensor networks,” Sensors (Basel), vol. 22, no. 2, p. 411, Jan. 6 2022. DOI: 10.3390/s22020411
[13]A. E. Guerrero-Sanchez, E. A. Rivas-Araiza, J. L. Gonzalez-Cordoba, M. Toledano-Ayala, and A. Takacs, “Blockchain mechanism and symmetric encryption in a wireless sensor network,” Sensors (Basel), vol. 20, no. 10, p. 2798, May 14 2020.
[14]V. L. Vinya, Y. Anuradha, H. R. Karimi, P. B. Divakarachari, and V. Sunkari, “A Novel Blockchain Approach for Improving the Security and Reliability of Wireless Sensor Networks Using Jellyfish Search Optimizer,” Electronics (Basel), vol. 11, no. 21, p. 3449, 2022. DOI: 10.3390/electronics11213449
[15]D. P. Rajan, J. Premalatha, S. Velliangiri, and P. Karthikeyan, “Blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered‐based energy efficient routing protocol in wireless sensor network,” Trans. Emerg. Telecommun. Technol., vol. 33, no. 7, p. e4502, 2022. DOI: 10.1002/ett.4502
[16]M. Nouman, U. Qasim, H. Nasir, A. Almasoud, M. Imran, and N. Javaid, “Malicious node detection using machine learning and distributed data storage using blockchain in WSNs,” IEEE Access, vol. 11, pp. 6106–6121, 2023. DOI: 10.1109/ACCESS.2023.3236983
[17]M. A. Elsadig, “Detection of Denial-of-Service Attack in Wireless Sensor Networks: A lightweight Machine Learning Approach,” IEEE Access, vol. 11, pp. 83537–83552, 2023. DOI: 10.1109/ACCESS.2023.3303113
[18]M. A. Talukder, S. Sharmin, M. A. Uddin, M. M. Islam, and S. Aryal, “MLSTL-WSN: Machine learning-based intrusion detection using SMOTETomek in WSNs,” Int. J. Inf. Secur., vol. 23, no. 3, pp. 2139–2158, 2024. DOI: 10.1007/s10207-024-00833-z
[19]V. Shakya, J. Choudhary, and D. P. Singh, “IRADA: Integrated reinforcement learning and deep learning algorithm for attack detection in wireless sensor networks,” Multimedia Tools Appl., vol. 83, no. 28, pp. 1–20, 2024. DOI: 10.1007/s11042-024-18289-7
[20]M. Dener, C. Okur, S. Al, and A. Orman, “Wsn-bfsf: A new dataset for attacks detection in wireless sensor networks,” IEEE Internet Things J., 2023.
[21]T. M. Nguyen, H. H. P. Vo, and M. Yoo, “Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach,” Sensors (Basel), vol. 24, no. 11, p. 3339, May 23 2024. DOI: 10.3390/s24113339
[22]S. R. Jena, M. Z. U. Rahman, D. K. Sinha, V. Vimal, K. U. Singh, T. Syamsundararao, et al., “An Innovative Secure and Privacy-Preserving Federated Learning Based Hybrid Deep Learning Model for Intrusion Detection in Internet-Enabled Wireless Sensor Networks,” IEEE Trans. Consum. Electron. p. 1, 2024. DOI: 10.1109/TCE.2024.3442015
[23]L. Li, Z. Liang, X. Liang, and S. Li, “Monocular Depth Estimation Based on Residual Pooling and Global-local Feature Fusion,” IEEE Access, vol. 12,122785–122794, 2024. DOI: 10.1109/ACCESS.2024.3453942
[24]K. Dhanushkodi, R. Sethuraman, P. Mariappan, and A. Govindarajan, “An efficient cat hunting optimization-biased ReLU neural network for healthcare monitoring system,” Wirel. Netw., vol. 29, no. 8, pp. 3349–3365, 2023. DOI: 10.1007/s11276-023-03373-x
[25]E. H. Houssein, D. Oliva, E. Celik, M. M. Emam, and R. M. Ghoniem, “Boosted sooty tern optimization algorithm for global optimization and feature selection,” Expert Syst. Appl., vol. 213, p. 119015, 2023. DOI: 10.1016/j.eswa.2022.119015
[26]M. Liang, P. Cao, and J. Tang, “Rolling bearing fault diagnosis based on feature fusion with parallel convolutional neural network,” Int. J. Adv. Manuf. Technol., vol. 112, no. 3, pp. 819–831, 2021. DOI: 10.1007/s00170-020-06401-8
[27]X. Wang, Y. Zhu, H. Shi, Y. Liu, and C. Hong, (2024). Graph Triple Attention Network: A Decoupled Perspective. arXiv preprint arXiv:.
[28]A. S. Desuky, M. A. Cifci, S. Kausar, S. Hussain, and L. M. El Bakrawy, “Mud Ring Algorithm: A new meta-heuristic optimization algorithm for solving mathematical and engineering challenges,” IEEE Access, vol. 10, pp. 50448–50466, 2022. DOI: 10.1109/ACCESS.2022.3173401
[29]https://www.kaggle.com/datasets/bassamkasasbeh1/wsnds
[30]https://www.kaggle.com/datasets/celilokur/wsnbfsfdataset

International Journal of Computer Network and Information Security (IJCNIS)