基于自适应集成学习的异常流量检测OACSTPCD

We propose an adaptive integrate-learning-based anomalous traffic detection method in this paper that uses the discrete Fourier transform to extract the frequency domain features of traffic,resulting in less information loss during the extraction of traffic features.An evaluation metric based on stability and accuracy fluctuations is used to dynamically assess the reliability of the current traffic features,and the feature data blocks that pass the evaluation are used to generate new sub-classifiers.Meanwhile,an integrated adaptive classifier is designed,whose parameters and sub-classifiers are adjusted in real time according to the current situation.The experimental results show that the method is effective for solving the concept drift problem in anomalous traffic detection and machine learning against attacks.