A traffic anomaly detection approach in communication networks for applications of multimedia medical devices

Anomalous or unnormal multimedia medical devices are to yield anomaly network traffic and affect the diagnosis about medical issues. How to find anomaly network traffic is significantly important for normal applications of multimedia medical devices. This paper studies traffic anomaly detection problem in large-scale communication networks with multimedia medical devices. We employ empirical mode decomposition method and wavelet packet transform to propose an accurate detection method to capture it. Firstly, we use the wavelet packet transform to pre-treat network traffic. Network traffic is decomposed into multiple narrowband signals exhibiting more detailed features of network traffic. Secondly, the empirical mode decomposition method is utilized to divide these narrowband signals into the intrinsic mode function at different scales, in time and time-frequency domains. We calculate the spectral kurtosis value of the intrinsic mode function at these different scales to remove false components of the empirical mode decomposition. As a result, we can obtain new time and time-frequency signals which highlight the hidden nature of anomaly network traffic. Thirdly, we perform the reconstruction of empirical mode decompositions and wavelet packet transforms for the above time and time-frequency signals to attain a series of new time signals. Then we can find and diagnose abnormal network traffic. Simulation results show that our method is effective and promising.