Abstract: The data in the distributed network comes from different levels and devices, which makes the data have the heterogeneity of time series. Because the network state and traffic pattern change rapidly, it is difficult for the clustering results to accurately reflect the real distribution of data, affecting the accuracy of anomaly detection. Therefore, a cross-layer anomaly detection method for distributed network data based on improved extreme learning machine is proposed. Based on information entropy, the information volume of distributed network data is calculated to obtain the optimal probability distribution. Combined with sliding window technology, the weight attenuation function under the optimal probability distribution is constructed to achieve the distributed network data clustering, so as to accurately reflect the real distribution of data. The PReLU (Parametric Rectified Linear Unit) activation function is introduced to optimize the algorithm of the extreme learning machine, and the data after cluster processing is used as the input of the improved extreme learning machine to complete the cross-layer anomaly detection and improve the accuracy of anomaly detection. The experimental results show that the proposed method can effectively improve the cross-layer anomaly detection of distributed network data.

Key words: improving extreme learning machines, distributed network data, cross layer, anomaly detection