Hybrid Intrusion Detection Method based on Improved Adaboost and Enhanced SVM for Anomaly Detection in Wireless Sensor Networks

Mohammad Sirajuddin, Dr.B. Sateesh Kumar


The utilisation of Wireless Sensor Networks is quickly rising due to the fast progress of wireless sensor technologies. Due to limited resources, infrastructureless nature, and other factors, it faces major security difficulties. This study describes a hybrid IDS based on an improved AdaBoost and Enhanced SVM strategy for detecting network intrusions and monitoring node activity while classifying it as normal or abnormal. AdaBoost is used in combination with an SVM classifier to identify and classify intrusions. The suggested IDS considerably enhanced the network performance by recognising and eliminating malicious nodes from the network and avoiding DoS and sinkhole attacks. Results oproved that it performes better than other state of art methods in terms of transmission delay, detection rate, energy consumption, packet delivery rate. It also has the advantages of a simple structure and quick computation times.


IDS; WSN Security; Improved Adaboost; SVM; Hybrid IDS

Full Text:



N. Tran, H. Chen, J. Bhuyan and J. Ding, "Data Curation and Quality Evaluation for Machine Learning-Based Cyber Intrusion Detection," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3211313.

Sirajuddin, M., Sateesh Kumar, B. (2022). Collaborative Security Schemes for Wireless Sensor Networks. In: Kumar, A., Mozar, S. (eds) ICCCE 2021. Lecture Notes in Electrical Engineering, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-16-7985-8_36

H. Kawaguchi, Y. Nakatani and S. Okada, "IDPS signature classification based on active learning with partial supervision from network security experts," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3211651.

M. Sirajuddin and B. S. Kumar, "Efficient and Secured Route Management Scheme Against Security Attacks in Wireless Sensor Networks," 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 2021, pp. 1045-1051, doi: 10.1109/ICESC51422.2021.9532779.

Murugan K, Suresh P. Ensemble of Ada Booster with SVM Classifier for Anomaly Intrusion Detection in Wireless Ad Hoc Network[J]. Indian Journal of Science and Technology, 2017, 10(21):1-10.

Dai Jianjian, Tao Yang, Yang Feiyue, A Novel Intrusion Detection System based on IABRBFSVM for Wireless Sensor Networks,

Procedia Computer Science, Volume 131, 2018, Pages 1113-1121, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2018.04.275.

Y. Yang, K. Yin and J. Yang, "Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3210189.

Ga Hyeon An, Ta Ho Cho, Improving Sink hole attack detection rate through knowledge based specification Rule for a sink hole attack Intrusion detection technique for IoT, International Journal of Computer Networks and Applications (IJCNA), Volume 9, Issue 2, March – April (2022), DOI: 10.22247/ijcna/2022/212333.

Quanmin Wang,Xuan Wei, The Detection of Network Intrusion Based on Improved Adaboost Algorithm ICCSP 2020: Proceedings of the 2020 4th International Conference on Cryptography, Security and Privacy,https://doi.org/10.1145/3377644.3377660

P. R. Chandre, P. N. Mahalle and G. R. Shinde, "Machine Learning Based Novel Approach for Intrusion Detection and Prevention System: A Tool Based Verification," 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN), 2018, pp. 135-140, doi: 10.1109/GCWCN.2018.8668618.

S. Otoum, B. Kantarci and H. T. Mouftah, "On the Feasibility of Deep Learning in Sensor Network Intrusion Detection," in IEEE Networking Letters, vol. 1, no. 2, pp. 68-71, June 2019, doi: 10.1109/LNET.2019.2901792.

DOI: https://doi.org/10.26483/ijarcs.v13i5.6912


  • There are currently no refbacks.

Copyright (c) 2022 International Journal of Advanced Research in Computer Science