Security Threat Prediction in WSNs Using Stacked Machine Learning Technique
Keywords:
Wireless Sensor Networks (WSNs), Machine Learning, Ensemble Learning, Intrusion Detection System (IDS), Threat Prediction, XGBoostAbstract
Wireless Sensor Networks (WSNs) have become vital for diverse applications such as military monitoring, healthcare, and urban traffic analysis. However, challenges like limited battery power, overlapping coverage, and energy dissipation hinder their performance and security. Traditional intrusion detection methods, including rule-based and cryptographic approaches, often struggle with adaptability or computational overhead in resource-constrained WSNs. Deep learning models, while effective, are typically too heavy for real-time deployment. To overcome these issues, this study proposes a stacked ensemble machine learning framework combining Decision Trees, Random Forest, XGBoost, and SVM classifiers. This approach leverages the strengths of multiple models via a meta-classifier to improve threat prediction accuracy, adaptability, and energy efficiency. Evaluated on standard WSN intrusion detection datasets, the framework achieves over 99.7% accuracy with high F1-scores and ROC-AUC, demonstrating superior detection of attacks like Blackhole, Flooding, Grayhole, and TDMA. The results highlight the method’s potential for scalable, lightweight, and robust real-time WSN security applications
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A. Daniel, K. M. Balamurugan, R. Vijay, K. Arjun, Energy aware clustering with multihop routing algorithm for wireless sensor networks, Intell. Autom. Soft Comput., 29 (2021), 233–246.
H. Li, J. Liu, Double cluster-based energy efficient routing protocol for wireless sensor network, Int. J. Wireless Inf. Netw., 23 (2016), 40–48. https://doi.org/10.1007/s10776-016-0300-9
B. Balakrishnan, S. Balachandran, FLECH: Fuzzy logic-based energy efficient clustering hierarchy for non-uniform wireless sensor networks, Wirel. Commun. Mob. Comput., 2017 (2017), 1214720. http://doi.org/10.1155/2017/1214720
T. Y. Kord, M. U. Bokhari, SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network, Wireless Netw., 22 (2016), 647–653. https://doi.org/10.1007/s11276-015-0997-x
F. A. Khan, A. Ahmad, M. Imran, Energy optimization of PR-LEACH routing scheme using distance awareness in internet of things networks, Int. J. Parallel Prog., 48 (2018), 244–263. https://doi.org/10.1007/s10766-018-0586-6
S. R. Biradar and P. D. Nair, “Detection and Prevention of Blackhole, Grayhole and Flooding Attacks in Wireless Sensor Networks: A Survey,” International Journal of Computer Applications, vol. 115, no. 4, pp. 26–33, Apr. 2015. DOI: 10.5120/20356-6593
M. M. Shurman, Z. Alomari, K. Mhaidat, K. An efficient billing scheme for trusted nodes using fuzzy logic in wireless sensor networks, J. Wirel. Eng. Technol., 5 (2014), 62–73. https://doi.org/10.4236/wet.2014.53008
A. Jain, A. K. Goel, Energy efficient fuzzy routing protocol for wireless sensor networks, Wireless Pers. Commun., 110 (2020), 1459–1474. https://doi.org/10.1007/s11277-019-06795-z
Thakkar, A., & Lohiya, R. (2021). Attack classification using feature selection techniques: a comparative study. Journal of Ambient Intelligence and Humanized Computing, 12(1), 1249–1266.
Thaseen, I. S., Kumar, C., Ahmad, A., et al. (2019). Integrated intrusion detection model using chi-square feature selection and ensemble of classifiers. Arabian Journal for Science and Engineering, 44(4), 3357–3368.
Türk, F. (2023). Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12(2), 465–477.
Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175–186.
Verma, J., Bhandari, A., & Singh, G. (2022). INIDS: SWOT analysis and TOWS inferences of state-of-the-art NIDS solutions for the development of intelligent network intrusion detection system: Vol. 195, (pp. 227–247). Elsevier.
Verma, R., & Chandra, S. (2023). REPUTE: A soft voting ensemble learning framework for reputation-based attack detection in Fog-IoT milieu: Vol. 118, Elsevier, Article 105670.
Vibhute, A. D., Patil, C. H., Mane, A. V., & Kale, K. V. (2024). Towards detection of network anomalies using machine learning algorithms on the NSL-KDD benchmark datasets. Procedia Computer Science, 233, 960–969.
Yulianto, A., Sukarno, P., & Suwastika, N. A. (2019). Improving AdaBoost-based intrusion detection system (IDS) performance on CIC-IDS-2017 dataset. Journal of Physics: Conference Series, 1192, Article 012018.
Zakariah, M., AlQahtani, S. A., Alawwad, A. M., & Alotaibi, A. A. (2023). Intrusion detection system with customized machine learning techniques for NSL-KDD dataset. Computers, Materials & Continua, 77(3).
Shabbir, N., Vassiljeva, K., Nourollahi Hokmabad, H., Husev, O., Petlenkov, E., & Belikov, J. (2024). Comparative analysis of machine learning techniques for non-intrusive load monitoring. Electronics, 13(8), 1420. https://doi.org/10.3390/electronics13081420
Zhang, Z., Zhang, Y., Wen, Y., et al. (2023). Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures. Complex & Intelligent Systems, 9, 5881–5892. https://doi.org/10.1007/s40747-023-01061-z
Faiz, M., & Daniel, A. K. (2022). A multi-criteria dual membership cloud selection model based on fuzzy logic for QoS. International Journal of Computing and Digital Systems, 12(1), 453-467.
Mounika, B. G., Faiz, M., Fatima, N., & Sandhu, R. (2024). A robust hybrid deep learning model for acute lymphoblastic leukemia diagnosis. In Advances in Networks, Intelligence and Computing (pp. 679-688). CRC Press.
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