| Summary: | In order to effectively deal with the security threats of home and personal Internet of Things(IoT) bot nets,especially for the objective problem of insufficient resources for anomaly detection in the home environment,a kernel density estimation-based lightweight IoT anomaly traffic detection (KDE-LIATD) method is proposed.Firstly,the KDE-LIATD method uses a Gaussian kernel density estimation method to estimate the probability density function and corresponding probability density of each dimension feature value of thenormal samples in the training set.Then,a kernel density estimation-based feature selection algorithm (KDE-FS) is proposed to obtain features that contribute significantly to anomaly detection,thereby reducing the feature dimension while improving the accuracy of anomaly detection.Finally,the cubic spline interpolation method is used to calculate the anomaly evaluation value of the test sample and perform anomaly detection.This strategy greatly reduces the computational overhead and storage overhead required to calculate the anomaly evaluation value of the test sample using the kernel density estimation method.Simulation experiment results show that the KDE-LIATD method has strong robustness and strong compatibility for anomaly traffic detection of heterogeneous IoT devices,and can effectively detect abnormal traffic in home and personal IoT bot nets.
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