Web traffic refers to the amount of data that is sent and received by people visiting online websites. Web traffic anomalies represent abnormal changes in time series traffic, and it is important to perform detection quickly and accurately for the efficient operation of complex computer networks systems. In this paper, we propose a C-LSTM neural network for effectively modeling the spatial and temporal information contained in traffic data, which is a one-dimensional time series signal. We also provide a method for automatically extracting robust features of spatial-temporal information from raw data. Experiments demonstrate that our C-LSTM method can extract more complex features by combining a convolutional neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN). The CNN layer is used to reduce the frequency variation in spatial information; the LSTM layer is suitable for modeling time information; and the DNN layer is used to map data into a more separable space. Our C-LSTM method also achieves nearly perfect anomaly detection performance for web traffic data, even for very similar signals that were previously considered to be very difficult to classify. Finally, the C-LSTM method outperforms other state-of-the-art machine learning techniques on Yahoo's well-known Webscope S5 dataset, achieving an overall accuracy of 98.6% and recall of 89.7% on the test dataset.
Bibliographical noteFunding Information:
This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract ( UD160066BD ).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence