IoT botnet detection with feature reconstruction and interval optimization

Abstract

The existing botnet detection methods have the problems of uneven sampling, poor feature selection, and weak generalization ability, resulting in low detection and classification results and poor adaptability to the internet of things (IoT) environment with limited computing and storage resources. This paper proposes an IoT botnet detection method using feature reconstruction and interval optimization to solve the above problems. Through the designed address triple and time window-based IP aggregation and feature reconstruction method (ATTW-IP-FR), the network traffic samples obtained from the IoT gateway are integrated, and the flow features are reconstructed to attain the reconstructed sample set. The proposed self-corrected hybrid weighted sampling algorithm balances the normal and botnet flow samples in the reconstructed sample set to get the resampling sample set. The introduced multiattribute decision-making and adjacency relation chain-based sequential forward selection algorithm is applied to eliminate the redundant features in the resampling sample set, and the optimal feature subset is obtained. The resampling sample set filtered by the optimal feature subset is detected and classified through the designed two-stage hybrid heterogeneous model optimized by the intermittent chaos and bald eagle search algorithm-based interval optimization algorithm. The experimental results show that the proposed method effectively detects the botnet in two real IoT scenarios. The detection accuracy is 99.17 % $ \% $ , the Matthews correlation coefficient is 98.35 % $ \% $ , the false positive rate is 0.25 % $ \% $ , and the false negative rate is 1.27 % $ \% $ , which are better than the existing methods. This method can effectively reduce sampling and feature selection time and space overhead and better adapt to the resource-constrained IoT environment.