Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in the Internet of Things (IoT) network. The followings are the major contribu-tions: i) An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network; ii) An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space; iii) Finally, the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency. Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

Automated summary

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed for identifying intrusive activities in the Internet of Things (IoT) network. The major contributions are: i) the development of an optimized LGBM model for identifying malicious IoT activities; ii) the adoption of an efficient evolutionary optimization approach for finding the optimal set of hyper-parameters of LGBM; iii) the evaluation of the proposed model using state-of-the-art ensemble learning and machine learning-based models. Simulation outcomes show that the proposed approach outperforms other methods and proves to be a robust approach for intrusion detection in an IoT environment.