Quantile Recursive-Random Outlier Imputation With Wrapper Boruta For Instrusion Detection In WSNs
DOI:
https://doi.org/10.52783/jns.v14.3088Keywords:
Feature selection, Intrusion detection, Light GBM, Quantile recursive, PreprocessingAbstract
Wireless Sensor Networks (WSNs) are increasingly vulnerable to variety of security threats, making the detection of network intrusions critical. This study presents a novel approach for Intrusion Detection (ID) in WSNs by combining the advanced data pre-processing and feature selection techniques. Quantile Recursive-Random Outlier Imputing (QR-ROI) algorithm is used for effective data pre-processing, addressing missing or anomalous values in wsnds dataset, which is collected from the Kaggle repository. To further optimize the model's accuracy and efficiency, feature selection is conducted using the Wrapper Boruta Algorithm, enhanced with Light Gradient Boosting Machine (GBM), known for its speed and performance. The integration of these techniques not only improves the quality of dataset but also enhances the overall predictive accuracy to 94.12 of Intrusion Detection System (IDS). Experimental results demonstrate the efficacy of this approach in distinguishing between normal and malicious activities within WSN environments.
Downloads
Metrics
References
Almomani, I., Al-Kasasbeh, B., & Al-Akhras, M. (2016). WSN‐DS: a dataset for intrusion detection systems in wireless sensor networks. Journal of Sensors, 2016(1), 4731953.
Ghosh, K., Neogy, S., Das, P. K., & Mehta, M. (2018). Intrusion detection at international borders and large military barracks with multi-sink wireless sensor networks: An energy efficient solution. Wireless Personal Communications, 98, 1083-1101.
Bagwari, A., Logeshwaran, J., Usha, K., Kannadasan, R., Alsharif, M. H., Uthansakul, P., & Uthansakul, M. (2023). An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning. IEEE Access.
Rao, A. K., Nagwanshi, K. K., & Shukla, M. K. (2024). An optimized secure cluster-based routing protocol for IoT-based WSN structures in smart agriculture with blockchain-based integrity checking. Peer-to-Peer Networking and Applications, 17(5), 3159-3181.
Karthikeyan, M., Manimegalai, D., & RajaGopal, K. (2024). Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection. Scientific Reports, 14(1), 231.
Bapat, V., Kale, P., Shinde, V., Deshpande, N., & Shaligram, A. (2017). WSN application for crop protection to divert animal intrusions in the agricultural land. Computers and electronics in agriculture, 133, 88-96.
Stehlik, M., Saleh, A., Stetsko, A., & Matyas, V. (2013, September). Multi-objective optimization of intrusion detection systems for wireless sensor networks. In Artificial Life Conference Proceedings (pp. 569-576). One Rogers Street, Cambridge, MA 02142-1209, USA journals-info@ mit. edu: MIT Press.
Elsaid, S. A., & Albatati, N. S. (2020). An optimized collaborative intrusion detection system for wireless sensor networks. Soft Computing, 24(16), 12553-12567.
Stetsko, A., Smolka, T., Matyáš, V., & Stehlík, M. (2014). Improving intrusion detection systems for wireless sensor networks. In Applied Cryptography and Network Security: 12th International Conference, ACNS 2014, Lausanne, Switzerland, June 10-13, 2014. Proceedings 12 (pp. 343-360). Springer International Publishing.
Batra, I., Verma, S., Kavita, & Alazab, M. (2020). A lightweight IoT‐based security framework for inventory automation using wireless sensor network. International journal of communication systems, 33(4), e4228.
Reddy, S. V. V., Manonmani, S. P., Anitha, C., Jaganathan, D., Reena, R., & Suresh, M. (2024, March). MLIDS: Revolutionizing of IoT based Digital Security Mechanism with Machine Learning Assisted Intrusion Detection System. In 2024 International Conference on Automation and Computation (AUTOCOM) (pp. 277-282). IEEE.
Kangethe, L., Wimmer, H., & Rebman Jr, C. M. (2024). Network Intrusion Detection System with Machine Learning as a Service. Journal of Information Systems Applied Research, 17(3).
Ahmed, O. (2024). Enhancing Intrusion Detection in Wireless Sensor Networks through Machine Learning Techniques and Context Awareness Integration. International Journal of Mathematics, Statistics, and Computer Science, 2, 244-258.
Saleh, H. M., Marouane, H., & Fakhfakh, A. (2024). Stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning. IEEE Access.
Gnanaprasanambikai, L., & Munusamy, N. (2018). Data pre-processing and classification for traffic anomaly intrusion detection using NSLKDD dataset. Cybernetics and Information Technologies, 18(3), 111-119.
Durairaj. M and D. Radhika, Pre-Processing Techniques for Enhancing the Performance of Intrusion Detection System. International Journal of Electrical Engineering and Technology, 11(5), 2020, pp. 191-206.
Grando, F., Granville, L. Z., & Lamb, L. C. (2018). Machine learning in network centrality measures: Tutorial and outlook. ACM Computing Surveys (CSUR), 51(5), 1-32.
Di Mauro, M., Galatro, G., Fortino, G., & Liotta, A. (2021). Supervised feature selection techniques in network intrusion detection: A critical review. Engineering Applications of Artificial Intelligence, 101, 104216.
Alazab, A., Hobbs, M., Abawajy, J., & Alazab, M. (2012, October). Using feature selection for intrusion detection system. In 2012 international symposium on communications and information technologies (ISCIT) (pp. 296-301). IEEE.
Abdullah, M., Alshannaq, A., Balamash, A., & Almabdy, S. (2018). Enhanced intrusion detection system using feature selection method and ensemble learning algorithms. International Journal of Computer Science and Information Security (IJCSIS), 16(2), 48-55.
Aburomman, A. A., & Reaz, M. B. I. (2017). A survey of intrusion detection systems based on ensemble and hybrid classifiers. Computers & security, 65, 135-152.
Sharma, N. V., & Yadav, N. S. (2021). An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers. Microprocessors and Microsystems, 85, 104293.
Pereda-Fernández, S. (2024). Fast Algorithms for Quantile Regression with Selection. arXiv preprint arXiv:2402.16693.
Sabitha, E., & Durgadevi, M. (2022). Improving the diabetes Diagnosis prediction rate using data preprocessing, data augmentation and recursive feature elimination method. International Journal of Advanced Computer Science and Applications, 13(9).
Karrar, A. E. (2022). The effect of using data pre-processing by imputations in handling missing values. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10(2), 375-384.
Kursa, M. B., Jankowski, A., & Rudnicki, W. R. (2010). Boruta–a system for feature selection. Fundamenta Informaticae, 101(4), 271-285.
LI, Z. S., YAO, X., LIU, Z. G., & ZHANG, J. C. (2021). Feature selection algorithm based on LightGBM. Journal of Northeastern University (Natural Science), 42(12), 1688.
Alsaffar, A. M., Nouri-Baygi, M., & Zolbanin, H. M. (2024). Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning. Journal of Big Data, 11(1), 133.
Maldonado, J., Riff, M. C., & Neveu, B. (2022). A review of recent approaches on wrapper feature selection for intrusion detection. Expert Systems with Applications, 198, 116822.
Liu, Z., Wei, W., Wang, H., Zhang, Y., Zhang, Q., & Li, S. (2018). Intrusion detection based on parallel intelligent optimization feature extraction and distributed fuzzy clustering in WSNs. IEEE Access, 6, 72201-72211.
Ahmad, I. (2015). Feature selection using particle swarm optimization in intrusion detection. International Journal of Distributed Sensor Networks, 11(10), 806954.
Aljebreen, M., Alohali, M. A., Saeed, M. K., Mohsen, H., Al Duhayyim, M., Abdelmageed, A. A., ... & Abdelbagi, S. (2023). Binary chimp optimization algorithm with ML based intrusion detection for secure IoT-assisted wireless sensor networks. Sensors, 23(8), 4073.
Hasan, M. A. M., Nasser, M., Ahmad, S., & Molla, K. I. (2016). Feature selection for intrusion detection using random forest. Journal of information security, 7(3), 129-140.
Liu, G., Zhao, H., Fan, F., Liu, G., Xu, Q., & Nazir, S. (2022). An enhanced intrusion detection model based on improved kNN in WSNs. Sensors, 22(4), 1407.
Safaldin, M., Otair, M., & Abualigah, L. (2021). Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. Journal of ambient intelligence and humanized computing, 12, 1559-1576.
Singh, N., Virmani, D., & Gao, X. Z. (2020). A fuzzy logic-based method to avert intrusions in wireless sensor networks using WSN-DS dataset. International Journal of Computational Intelligence and Applications, 19(03), 2050018.
Brahmam, M. V., & Gopikrishnan, S. (2024). Adaptive threshold based outlier detection on IoT sensor data: A node-level perspective. Alexandria Engineering Journal, 106, 675-690.
Gebremariam, G. G., Panda, J., & Indu, S. (2023). Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks. Connection Science, 35(1), 2246703.
Faris, M., Mahmud, M. N., Salleh, M. F. M., & Alsharaa, B. (2023). A differential evolution-based algorithm with maturity extension for feature selection in intrusion detection system. Alexandria Engineering Journal, 81, 178-192.
Nguyen, T. M., Vo, H. H. P., & Yoo, M. (2024). Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach. Sensors, 24(11), 3339.
Samadi Bonab, M., Ghaffari, A., Soleimanian Gharehchopogh, F., & Alemi, P. (2020). A wrapper‐based feature selection for improving performance of intrusion detection systems. International Journal of Communication Systems, 33(12), e4434.
Vijayanand, R., & Devaraj, D. (2020). A novel feature selection method using whale optimization algorithm and genetic operators for intrusion detection system in wireless mesh network. IEEE Access, 8, 56847-56854.
Harita, U., & Mohammed, M. (2024). Modelling an Improved Swarm Optimizer and Boosted Quantile Estimator For Malicious Flow Monitoring And Prediction In Network. Journal of Cybersecurity & Information Management, 13(2).
Wu, L., & Qu, J. (2023). AIMD rule-based duty cycle scheduling in wireless sensor networks using quartile-directed adaptive genetic algorithm. IEEE Sensors Journal, 23(5), 4905-4921.
Subbiah, S., Anbananthen, K. S. M., Thangaraj, S., Kannan, S., & Chelliah, D. (2022). Intrusion detection technique in wireless sensor network using grid search random forest with Boruta feature selection algorithm. Journal of Communications and Networks, 24(2), 264-273.
Bhutta, A. A., Nisa, M. U., & Mian, A. N. (2024). Lightweight real-time WiFi-based intrusion detection system using LightGBM. Wireless Networks, 30(2), 749-761.
Prajisha, C., & Vasudevan, A. R. (2022). An efficient intrusion detection system for MQTT-IoT using enhanced chaotic salp swarm algorithm and LightGBM. International Journal of Information Security, 21(6), 1263-1282.
Liu, J., Gao, Y., & Hu, F. (2021). A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM. Computers & Security, 106, 102289.
Meng, D., Dai, H., Sun, Q., Xu, Y., & Shi, T. (2022). Novel Wireless Sensor Network Intrusion Detection Method Based on LightGBM Model. IAENG International Journal of Applied Mathematics, 52(4).
Arellano, M., & Bonhomme, S. (2017). Quantile selection models with an application to understanding changes in wage inequality. Econometrica, 85(1), 1-28.
Shapoorifard, H., & Shamsinejad, P. (2017). Intrusion detection using a novel hybrid method incorporating an improved KNN. Int. J. Comput. Appl, 173(1), 5-9.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
