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3.2.3 Type of traffic prediction (traffic classification) Type of traffic prediction aka Network Traffic Classification (NTC) is an important part of current network management and administration systems. An NTC infers the service/application (e.g. FTP, Radius, LDAP...) being used by a network flow. This information is important for network management and Quality of Service (QoS), as the service used has a direct relationship with QoS requirements and user contracts/expectations. Network traffic identification is crucial for implementing effective management of network policy and resources in data networks, as the network needs to react differently depending on traffic profile information. There are several approaches to NTC: port-based, payload-based, and flow statistics-based [95][96]. Port-based methods make use of port information for service identification. These methods are not reliable as many services do not use well-known ports or even use the ports used by other applications. Payload-based approaches the problem by deep packet inspection (DPI) of the payload carried out by the communication flow. These methods look for well-known patterns inside the packets. They currently provide the best possible detection rates but with some associated costs and difficulties: the cost of relying on an up-to-date database of patterns (which must be maintained) and the difficulty to be able to access the raw payload. Currently, an increasing proportion of transmitted data is being encrypted or needs to assure user privacy policies, which is a real problem to payload-based methods. Finally, flow statistics-based methods rely on information that can be obtained from packets header (e.g. bytes transmitted, packets interarrival times, TCP window size,). They rely on packet header high-level information which makes them a better option to deal with non- available payloads or dynamic ports. These methods usually rely on machine learning techniques to perform service prediction [95]. The works presented here are based on flow statistics-based methods. There are many datasets available to carry out experiments for NTC (Moore, WIDE...). However, most of the experiments are done with proprietary traffic as it is the case for the research performed in this thesis. If we focus on works related with deep learning models, this thesis provides the first study, as far as we know, of a CNN+RNN model applied to NTC. There are many works that apply neural networks to NTC, but the network models employed are variants of MLP classifiers. In [97] they propose a multi-layer perceptron (MLP) with one hidden layer. An ensemble of MLP classifiers is applied in [98]. In [99] an MLP with a particle swarm optimization algorithm is employed. Zhou et al. [100] apply an MLP with 3 hidden layers. A Parallel Neural Network Classifier Architecture is used in [101], it is made up of parallel blocks of radial basis function neural networks. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 32PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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