Citation :

M. Pradeepa, R. Ponnusamy, "Advanced Ensemble Deep Learning Model Integrated with Metaheuristic Optimisation for Secure and Reliable Intrusion Detection in Wireless Sensor Networks," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 2, pp. 280-292, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I2P120

Abstract

With the growth of reliance on the internet and the transfer of most businesses to present remote services, the problems in protecting the network and identifying attacks rapidly become more prominent, as the attack surface and cyberattacks improve in response. The current Wireless Sensor Networks (WSNs) intrusion detection methods that utilize Machine Learning (ML) techniques to detect previously known attacks use single layers of recognition, which means an expensive algorithm must be performed before identifying any suspicious action. Network Intrusion Detection Systems (IDS) present a type of service to the system, and it becomes unavoidable for some communication systems. ML methods are extensively applied in IDS; still, the performance of ML methods is less adequate while processing unbalanced attacks. This paper presents an Advanced Ensemble Deep Learning Model Integrated with Metaheuristic Optimization for Secure and Reliable Intrusion Detection (AEDL-MORID) methodology. The main objective of the AEDL-MORID methodology presents strong potential for real-time deployment in resource-constrained WSN environments, strengthening network resilience against sophisticated cyber threats. The AEDL-MORID method starts with data pre-processing techniques involving the handling of missing values and min-max normalization to ensure clean and consistent input for the learning models. For dimensionality reduction, the dung beetle optimization (DBO) method is utilized to detect the most informative features effectively. In addition, an ensemble classification method integrating Bidirectional Long Short-Term Memory (BiLSTM), Graph Convolutional Network (GCN), and Stacked Denoising Autoencoder (SDAE) is employed for attack detection. To further improve ensemble classification performance, the model parameters are fine-tuned using the Improved Crow Search Algorithm (ICSA) method. The experimentation of the AEDL-MORID model is conducted on the WSN-DS dataset. The experimental validation of the AEDL-MORID system indicated a better accuracy of 99.81% compared to recent techniques.

Keywords

Intrusion Detection, Dung Beetle Optimization, Wireless Sensor Network, Attack Detection, Resource Constrained, Deep Learning.

References

[1] V. Gowdhaman, and R. Dhanapal, “An Intrusion Detection System for Wireless Sensor Networks using Deep Neural Network,” Soft Computing, vol. 26, no. 23, pp. 13059-13067, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Halima Sadia et al., “Intrusion Detection System for Wireless Sensor Networks: A Machine Learning based Approach,” IEEE Access, vol. 12, pp. 52565-52582, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[3] K. Sedhuramalingam, and N. Saravanakumar, “A Novel Optimal Deep Learning Approach for Designing Intrusion Detection System in Wireless Sensor Networks,” Egyptian Informatics Journal, vol. 27, pp. 1-8, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Fatima Al-Quayed, Zulfiqar Ahmad, and Mamoona Humayun, “A Situation based Predictive Approach for Cybersecurity Intrusion Detection and Prevention using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0,” IEEE Access, vol. 12, pp. 34800-34819, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Safa Otoum, Burak Kantarci, and Hussein Mouftah, “On the Feasibility of Deep Learning in Sensor Network Intrusion Detection,” IEEE Networking Letters, vol. 1, no. 2, pp. 68-71, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Abhilash Singh et al., “A Deep Learning Approach to Predict the Number of K-Barriers for Intrusion Detection Over a Circular Region using Wireless Sensor Networks,” Expert Systems with Applications, vol. 211, pp. 1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Bandar Almaslukh et al., “Deep Learning and Entity Embedding-based Intrusion Detection Model for Wireless Sensor Networks,” Computers, Materials & Continua, vol. 69, no. 1, pp. 1343-1360, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Liqun Yang et al., “Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism,” IEEE Access, vol. 8, pp. 170128-170139, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Remah Alshinina, and Khaled Elleithy, “A Highly Accurate Deep Learning based Approach for Developing Wireless Sensor Network Middleware,” IEEE Access, vol. 6, pp. 29885-29898, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Jyoti Srivastava, and Jay Prakash, “Deep Learning-Enabled Energy Optimization and Intrusion Detection for Wireless Sensor Networks,” Opsearch, vol. 62, no. 1, pp. 368-405, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Maryam Mahdi Alhusseini, Alireza Rouhi, and Mohammad-Reza Feizi-Derakhshi, “AI-Powered Hybrid Intrusion Detection Framework for Cloud Security using Novel Metaheuristic Optimization,” arXiv Preprint, pp. 1-18, 2026.
[CrossRef] [Google Scholar] [Publisher Link]

[12] M. Sakthimohan, J. Deny, and G. Elizabeth Rani, “Secure Deep Learning-Based Energy Efficient Routing with Intrusion Detection System for Wireless Sensor Networks,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 46, no. 4, pp. 8587-8603, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Ali Siddiq, and Yahya Jaber Ghazwani, “Hybrid Optimized Deep Neural Network-Based Intrusion Node Detection and Modified Energy Efficient Centralized Clustering Routing Protocol for Wireless Sensor Network,” IEEE Transactions on Consumer Electronics, vol. 70, no. 3, pp. 6303-6313, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Priyanka Pande, Harsh Mathur, and Lalit Kumar Gupta, “Machine Learning-Based Intrusion Detection System using Wireless Sensor Networks,” 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Hanjabam Saratchandra Sharma, Arindam Sarkar, and Moirangthem Marjit Singh, “An Efficient Deep Learning-Based Solution for Network Intrusion Detection in Wireless Sensor Network,” International Journal of System Assurance Engineering and Management, vol. 14, no. 6, pp. 2423-2446, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[16] S. Karthic, and S. Manoj Kumar, “Hybrid Optimized Deep Neural Network with Enhanced Conditional Random Field based Intrusion Detection on Wireless Sensor Network,” Neural Processing Letters, vol. 55, no. 1, pp. 459-479, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[17] M. Nirmal Kumar, T. Vijayan, and B. Karthik, “Intrusion Detection Framework for Industrial Wireless Sensor Networks in Smart Manufacturing,” Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems, pp. 197-218, 2026.
[Google Scholar]

[18] Alok Kumar Shukla, Shubhra Dwivedi, and Aishwarya Mishra, “An Effective Hybrid Deep Learning Metaheuristic Model for Robust IoT Intrusion Detection,” Discover Computing, vol. 28, no. 1, pp. 1-31, 2025.

[CrossRef] [Google Scholar] [Publisher Link]

[19] R. Pavithra Guru, Thomas M. Chen, and Mithileysh Sathiyanarayanan, “ABO Optimized Hybrid Trans-CNN-Bi-GRU Approach for Intrusion Detection in IoT Networks: A Privacy-Preserving Solution,” Cluster Computing, vol. 29, no. 1, 2026.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Sandeep Mahato, and Subrata Dutta, “Ensemble based Meta-Heuristic Optimized Approach for Network Intrusion Detection using LightGBM,” Cluster Computing, vol. 28, no. 12, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[21] YaHui Lv, “Research on Improving the Efficiency and Accuracy of Accounting Data Processing based on Intelligent Financial Software,” International Journal of High Speed Electronics and Systems, vol. 35, no. 2, pp. 1-24, 2026.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Shuxun Li et al., “Composite Noise Reduction Method for Internal Leakage Acoustic Emission Signal of Safety Valve based on IWTD-IVMD Algorithm,” Sensors, vol. 25, no. 15, pp. 1-32, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Bouchra Fatima Zohra Diaf et al., “A Novel Bidirectional Lstm-Based Approach for Wind Speed Forecasting: A Case Study of Oran and Adrar, Algeria,” SSRN Electronic Journal, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Adson Silva, and Ricardo Farias, “AD-VAE: Adversarial Disentangling Variational Autoencoder,” Sensors, vol. 25, no. 5, pp. 1-14, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Adil O. Khadidos, “Energy-Aware Unmanned Aerial Vehicle-Assisted Mobile Multimedia Communication via Metaheuristic Optimization with Stacked Deep Learning Models,” Alexandria Engineering Journal, vol. 129, pp. 864-876, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Maithili Shailesh Andhare et al., “A Novel Optimized Hybrid Deep Learning Framework for Mental Stress Detection using Electroencephalography,” Brain Sciences, vol. 15, no. 8, pp. 1-27, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Bassam Kasasbeh, Kaggle, 2026. [Online]. Available: https://www.kaggle.com/bassamkasasbeh1/datasets

[28] M. SriRaghavendra et al., “Knowledge Improved Hybrid DNN-KAN Framework for Intrusion Detection in Wireless Sensor Networks,” IEEE Access, vol. 13, pp. 127558-127569, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[29] Donghyeon Kim, Hyungchul Im, and Seongsoo Lee, “Adaptive Autoencoder-based Intrusion Detection System with Single Threshold for CAN Networks,” Sensors, vol. 25, no. 13, pp. 1-22, 2025.
[CrossRef] [Google Scholar] [Publisher Link]