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Hybrid Deep Embedding and LightGBM Meta-Classification for Multi-Class Intrusion Detection in Wireless Sensor Networks
Corresponding Author(s) : Ridha Muldina
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 4, November 2026 (Article in Progress)
Abstract
Internet of Things (IoT) applications require high service availability, and Wireless Sensor Networks (WSNs) are increasingly used in such applications, but WSNs are subject to Denial of Service (DoS) attacks that drain resources and interrupt communication. However, in such scenarios, the effectiveness of machine-learning-based Intrusion Detection Systems (IDS) was typically compromised by excessive class imbalance and inter-class overlap, resulting in high accuracy but low sensitivity to minority attack classes. In this research, we present a Hybrid Deep Embedding and LightGBM Meta-Classification system to improve multi-class IDS performance on the WSN-DS dataset. The contribution of each part was quantitatively evaluated using Stratified 5-Fold Cross-Validation in a five-stage experimental design: models, data balancing, loss/weighting strategies, optimal combination, and the suggested system. The complementary metric was reported as accuracy, and minority-aware metrics were applied, notably Macro-F1, balanced accuracy, and Matthews Correlation Coefficient (MCC). The results show that the independent use of data balancing and loss adjustment does not consistently improve performance in inter-class overlap scenarios. Conversely, the suggested system that learned deep embedding representations and modified the decision boundaries using a LightGBM meta-classifier demonstrated more consistent performance, with the best configuration achieving Macro-F1 of 0.977, balanced accuracy of 0.975 and MCC of 0.981. These results revealed that the improvement of representation separability and decision boundary flexibility was more consistent than the separate treatment of data imbalance in improving the performance of multi-class IDS.
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- I. Almomani, B. Al-Kasasbeh, and M. Al-Akhras, “WSN-DS: a dataset for intrusion detection systems in wireless sensor networks,” J. Sens., vol. 2016, no. 1, p. 4731953, 2016.
- M. Dener, S. Al, and A. Orman, “STLGBM-DDS: an efficient data balanced DoS detection system for wireless sensor networks on big data environment,” IEEE Access, vol. 10, pp. 92931–92945, 2022.
- Grand View Research, “Industrial Wireless Sensor Network Market Size, Share & Trends Analysis Report.” 2024. Accessed: Jan. 12, 2026. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/industrial-wireless-sensor-networks-iwsn-market
- R. Ahmad, R. Wazirali, Q. Bsoul, T. Abu-Ain, and W. Abu-Ain, “Feature-selection and mutual-clustering approaches to improve DoS detection and maintain WSNs’ lifetime,” Sensors, vol. 21, no. 14, p. 4821, 2021.
- I. M. Almomani and M. Alenezi, “Efficient Denial of Service Attacks Detection in Wireless Sensor Networks.,” J Inf Sci Eng, vol. 34, no. 4, pp. 977–1000, 2018.
- T. T. Lai, T. P. Tran, J. Cho, and M. Yoo, “DoS attack detection using online learning techniques in wireless sensor networks,” Alex. Eng. J., vol. 85, pp. 307–319, 2023.
- H. M. Saleh, H. Marouane, and A. Fakhfakh, “Stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning,” IEEe Access, vol. 12, pp. 3825–3836, 2024.
- S. E. Quincozes and J. F. Kazienko, “Machine learning methods assessment for denial of service detection in wireless sensor networks,” in 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), IEEE, 2020, pp. 1–6.
- A. Halbouni, T. S. Gunawan, M. H. Habaebi, M. Halbouni, M. Kartiwi, and R. Ahmad, “CNN-LSTM: hybrid deep neural network for network intrusion detection system,” IEEE Access, vol. 10, pp. 99837–99849, 2022.
- L. Alsulaiman and S. Al-Ahmadi, “Performance evaluation of machine learning techniques for DOS detection in wireless sensor network,” ArXiv Prepr. ArXiv210401963, 2021.
- A. A. Khan, O. Chaudhari, and R. Chandra, “A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation,” Expert Syst. Appl., vol. 244, p. 122778, 2024.
- I. D. Mienye and Y. Sun, “A survey of ensemble learning: Concepts, algorithms, applications, and prospects,” Ieee Access, vol. 10, pp. 99129–99149, 2022.
- P. Pajila et al., “Enhanced autoencoder and deep neural network (AE&DNN) method for ddos attack recognition,” Wirel. Netw., vol. 31, no. 4, pp. 3277–3295, 2025.
- G. Ke et al., “Lightgbm: A highly efficient gradient boosting decision tree,” Adv. Neural Inf. Process. Syst., vol. 30, 2017.
- S. Abiramasundari and V. Ramaswamy, “Distributed denial-of-service (DDOS) attack detection using supervised machine learning algorithms,” Sci. Rep., vol. 15, no. 1, p. 13098, Apr. 2025, doi: 10.1038/s41598-024-84879-y.
- M. Loughmari and A. El Affar, “A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks,” Int. J. Electr. Comput. Eng. IJECE, vol. 15, no. 2, pp. 2089–2097, 2025.
- R. R. Sani, F. A. Rafrastara, and W. Ghozi, “Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features,” Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, Jan. 2025, doi: 10.22219/kinetik.v10i1.2051.
- S. Salmi and L. Oughdir, “Cnn-lstm based approach for dos attacks detection in wireless sensor networks,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 4, 2022.
- S. Salmi and L. Oughdir, “Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network,” J. Big Data, vol. 10, no. 1, p. 17, 2023.
- B. Kasasbeh and H. Ahmad, “A Novel Sampling Technique for Detecting Cyber Denial of Service Attacks on the Internet of Things.,” in ICAART (3), 2023, pp. 861–868.
- 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.
- T. M. Nguyen, H. H.-P. Vo, and M. Yoo, “Enhancing intrusion detection in wireless sensor networks using a GSWO-CatBoost approach,” Sensors, vol. 24, no. 11, p. 3339, 2024.
- A. Sharma and S. Rani, “An Optimized Ensemble Approach for Securing Wireless Sensor Networks Against Attacks,” ICCK Trans. Wirel. Netw., vol. 1, no. 1, pp. 5–15, 2025.
- V. K. Pandey et al., “Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest,” Sci. Rep., vol. 15, no. 1, p. 18634, 2025.
- S. Ifzarne, H. Tabbaa, I. Hafidi, and N. Lamghari, “Anomaly detection using machine learning techniques in wireless sensor networks,” in Journal of Physics: Conference Series, IOP Publishing, 2021, p. 012021.
- O. Ahmed, “Enhancing intrusion detection in wireless sensor networks through machine learning techniques and context awareness integration,” Int. J. Math. Stat. Comput. Sci., vol. 2, pp. 244–258, 2024.
- H. Tabbaa, S. Ifzarne, and H. Imad, “An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks,” Comput. Inform., vol. 42, pp. 1013–1036, Dec. 2023, doi: 10.31577/cai_2023_4_1013.
- I. Sharafaldin, A. H. Lashkari, A. A. Ghorbani, and others, “Toward generating a new intrusion detection dataset and intrusion traffic characterization.,” ICISSp, vol. 1, no. 2018, pp. 108–116, 2018.
- M. Luay, S. Layeghy, S. Hosseininoorbin, M. Sarhan, N. Moustafa, and M. Portmann, “Temporal analysis of netflow datasets for network intrusion detection systems,” ArXiv Prepr. ArXiv250304404, 2025.
References
I. Almomani, B. Al-Kasasbeh, and M. Al-Akhras, “WSN-DS: a dataset for intrusion detection systems in wireless sensor networks,” J. Sens., vol. 2016, no. 1, p. 4731953, 2016.
M. Dener, S. Al, and A. Orman, “STLGBM-DDS: an efficient data balanced DoS detection system for wireless sensor networks on big data environment,” IEEE Access, vol. 10, pp. 92931–92945, 2022.
Grand View Research, “Industrial Wireless Sensor Network Market Size, Share & Trends Analysis Report.” 2024. Accessed: Jan. 12, 2026. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/industrial-wireless-sensor-networks-iwsn-market
R. Ahmad, R. Wazirali, Q. Bsoul, T. Abu-Ain, and W. Abu-Ain, “Feature-selection and mutual-clustering approaches to improve DoS detection and maintain WSNs’ lifetime,” Sensors, vol. 21, no. 14, p. 4821, 2021.
I. M. Almomani and M. Alenezi, “Efficient Denial of Service Attacks Detection in Wireless Sensor Networks.,” J Inf Sci Eng, vol. 34, no. 4, pp. 977–1000, 2018.
T. T. Lai, T. P. Tran, J. Cho, and M. Yoo, “DoS attack detection using online learning techniques in wireless sensor networks,” Alex. Eng. J., vol. 85, pp. 307–319, 2023.
H. M. Saleh, H. Marouane, and A. Fakhfakh, “Stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning,” IEEe Access, vol. 12, pp. 3825–3836, 2024.
S. E. Quincozes and J. F. Kazienko, “Machine learning methods assessment for denial of service detection in wireless sensor networks,” in 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), IEEE, 2020, pp. 1–6.
A. Halbouni, T. S. Gunawan, M. H. Habaebi, M. Halbouni, M. Kartiwi, and R. Ahmad, “CNN-LSTM: hybrid deep neural network for network intrusion detection system,” IEEE Access, vol. 10, pp. 99837–99849, 2022.
L. Alsulaiman and S. Al-Ahmadi, “Performance evaluation of machine learning techniques for DOS detection in wireless sensor network,” ArXiv Prepr. ArXiv210401963, 2021.
A. A. Khan, O. Chaudhari, and R. Chandra, “A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation,” Expert Syst. Appl., vol. 244, p. 122778, 2024.
I. D. Mienye and Y. Sun, “A survey of ensemble learning: Concepts, algorithms, applications, and prospects,” Ieee Access, vol. 10, pp. 99129–99149, 2022.
P. Pajila et al., “Enhanced autoencoder and deep neural network (AE&DNN) method for ddos attack recognition,” Wirel. Netw., vol. 31, no. 4, pp. 3277–3295, 2025.
G. Ke et al., “Lightgbm: A highly efficient gradient boosting decision tree,” Adv. Neural Inf. Process. Syst., vol. 30, 2017.
S. Abiramasundari and V. Ramaswamy, “Distributed denial-of-service (DDOS) attack detection using supervised machine learning algorithms,” Sci. Rep., vol. 15, no. 1, p. 13098, Apr. 2025, doi: 10.1038/s41598-024-84879-y.
M. Loughmari and A. El Affar, “A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks,” Int. J. Electr. Comput. Eng. IJECE, vol. 15, no. 2, pp. 2089–2097, 2025.
R. R. Sani, F. A. Rafrastara, and W. Ghozi, “Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features,” Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, Jan. 2025, doi: 10.22219/kinetik.v10i1.2051.
S. Salmi and L. Oughdir, “Cnn-lstm based approach for dos attacks detection in wireless sensor networks,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 4, 2022.
S. Salmi and L. Oughdir, “Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network,” J. Big Data, vol. 10, no. 1, p. 17, 2023.
B. Kasasbeh and H. Ahmad, “A Novel Sampling Technique for Detecting Cyber Denial of Service Attacks on the Internet of Things.,” in ICAART (3), 2023, pp. 861–868.
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.
T. M. Nguyen, H. H.-P. Vo, and M. Yoo, “Enhancing intrusion detection in wireless sensor networks using a GSWO-CatBoost approach,” Sensors, vol. 24, no. 11, p. 3339, 2024.
A. Sharma and S. Rani, “An Optimized Ensemble Approach for Securing Wireless Sensor Networks Against Attacks,” ICCK Trans. Wirel. Netw., vol. 1, no. 1, pp. 5–15, 2025.
V. K. Pandey et al., “Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest,” Sci. Rep., vol. 15, no. 1, p. 18634, 2025.
S. Ifzarne, H. Tabbaa, I. Hafidi, and N. Lamghari, “Anomaly detection using machine learning techniques in wireless sensor networks,” in Journal of Physics: Conference Series, IOP Publishing, 2021, p. 012021.
O. Ahmed, “Enhancing intrusion detection in wireless sensor networks through machine learning techniques and context awareness integration,” Int. J. Math. Stat. Comput. Sci., vol. 2, pp. 244–258, 2024.
H. Tabbaa, S. Ifzarne, and H. Imad, “An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks,” Comput. Inform., vol. 42, pp. 1013–1036, Dec. 2023, doi: 10.31577/cai_2023_4_1013.
I. Sharafaldin, A. H. Lashkari, A. A. Ghorbani, and others, “Toward generating a new intrusion detection dataset and intrusion traffic characterization.,” ICISSp, vol. 1, no. 2018, pp. 108–116, 2018.
M. Luay, S. Layeghy, S. Hosseininoorbin, M. Sarhan, N. Moustafa, and M. Portmann, “Temporal analysis of netflow datasets for network intrusion detection systems,” ArXiv Prepr. ArXiv250304404, 2025.