Abstract
The Internet has been evolving from a traditional mechanism to a modern service-oriented architecture, such as quality-of-service (QoS) policies, to meet users’ various requirements for high service quality. An instant and effective network traffic classification method is indispensable to identify network services to enforce QoS policies on the corresponding service. Network managers can easily flexibly deploy traffic classification modules and configure the network policies with the help of the emerging software-defined networking. However, most existing traffic classification solutions, such as port-based methods or deep packet inspection, cannot handle real-time and encrypted traffic classification. In this research, a Convolutional Autoencoder Packet Classifier (CAPC) has been proposed to immediately classify incoming packets in fine-grained and coarse-grained manners, that is, classifying a service to a single application and a rough genre, respectively. The CAPC is a packet-based deep learning model consisting of a 1D convolutional neural network and an autoencoder, which can handle dynamic-port and encrypted traffic and even cluster similar applications. This classifier is verified on not only the private self-captured traffic but also a public VPN dataset to demonstrate its performance. Moreover, the CAPC classifies different types of service traffic with an accuracy of over 99.9% on the private dataset of 16 services and over 97% on the public dataset of 24 services, thereby outperforming other deep learning classifiers. Experimental results also show other performance metrics, including stability, average precision, and recall and the highest F1-score values of 15 and 18 services on the private and public datasets, respectively.
Highlights
The Internet has been evolving from a traditional mechanism to a modern service-oriented architecture, such as quality-of-service (QoS) policies, to meet users’ various requirements for high service quality
A neural network-based classifier can to consider the relationship between packets in service traffic, 44 automatically update internal network parameters to increase which merely analyzes the payload of a packet
Reported that traffic classification methods can be roughly 49 The deep learning method is a prospective solution when categorized into three types, namely, port-based, payload-50 raw packets are taken as input data; even the hidden based, and machine learning (ML)-based methods. 51 relationship between bytes of encrypted traffic can be
Summary
With the advancement of the diversity of network services, of graphic processing unit (GPU), thereby significantly network functions demanded by users are not limited to data accelerating parallel computing. A neural network-based classifier can to consider the relationship between packets in service traffic, automatically update internal network parameters to increase which merely analyzes the payload of a packet. Under such the weights corresponding to significant flow features [43], circumstance, more packet data are required to extract [44]. Reported that traffic classification methods can be roughly 49 The deep learning method is a prospective solution when categorized into three types, namely, port-based, payload-50 raw packets are taken as input data; even the hidden based, and ML-based methods (summarized in Table I). relationship between bytes of encrypted traffic can be TABLE I Overview of traffic classification approaches discovered through the high-complexity computation [22],
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