A Model-Based System for Intrusion Detection Using Novel Technique-Hidden Markov Bayesian in Wireless Sensor Network
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-0739-4_4
Reference8 articles.
1. Tan, X., Su, S., Huang, Z., Guo, X., Zuo, Z., Sun, X., Li, L.: Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm. Sensors 19(1), 203 (2019)
2. Yang, G., Yu, X., Xu, L., Xin, Y., Fang, X.: An intrusion detection algorithm for sensor network based on normalized cut spectral clustering. PLoS ONE 14(10), e0221920 (2019). https://doi.org/10.1371/journal.pone.0221920
3. Alqahtani, M., Gumaei, A., Mathkour, H., Maher Ben Ismail, M.: A genetic-based extreme gradient boosting model for detecting intrusions in wireless sensor networks. Sensors 19(20), 4383 (2019)
4. Nema, A.: Innovative approach for improving intrusion detection using genetic algorithm with layered approach. In: Securing the Internet of Things: Concepts, Methodologies, Tools, and Applications, pp. 273–298. IGI Global (2020)
5. Vijayanand, R., Devaraj, D., Kannapiran, B.: Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Comput. Secur. 77, 304–314 (2018)
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