Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things
This addresses network management for Internet service providers dealing with IoT traffic, but it is incremental as it builds on existing CNN and LSTM methods.
The paper tackles network traffic classification for IoT by proposing a novel data representation as a video stream to enable time-distributed feature learning, resulting in a 10% improvement in classification performance.
The plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network data representation, treating the traffic data as a series of images. Thus, the network data is realized as a video stream to employ time-distributed (TD) feature learning. The intra-temporal information within the network statistical data is learned using convolutional neural networks (CNN) and long short-term memory (LSTM), and the inter pseudo-temporal feature among the flows is learned by TD multi-layer perceptron (MLP). We conduct experiments using a large data-set with more number of classes. The experimental result shows that the TD feature learning elevates the network classification performance by 10%.