A novel federated learning based lightweight sustainable IoT approach to identify abnormal traffic

Abstract

This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample. To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed. The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices. With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.