  Internet of Things (IoT) defines a network of devices connected to the
internet and sharing a massive amount of data between each other and a central
location. These IoT devices are connected to a network therefore prone to
attacks. Various management tasks and network operations such as security,
intrusion detection, Quality-of-Service provisioning, performance monitoring,
resource provisioning, and traffic engineering require traffic classification.
Due to the ineffectiveness of traditional classification schemes, such as
port-based and payload-based methods, researchers proposed machine
learning-based traffic classification systems based on shallow neural networks.
Furthermore, machine learning-based models incline to misclassify internet
traffic due to improper feature selection. In this research, an efficient
multilayer deep learning based classification system is presented to overcome
these challenges that can classify internet traffic. To examine the performance
of the proposed technique, Moore-dataset is used for training the classifier.
The proposed scheme takes the pre-processed data and extracts the flow features
using a deep neural network (DNN). In particular, the maximum entropy
classifier is used to classify the internet traffic. The experimental results
show that the proposed hybrid deep learning algorithm is effective and achieved
high accuracy for internet traffic classification, i.e., 99.23%. Furthermore,
the proposed algorithm achieved the highest accuracy compared to the support
vector machine (SVM) based classification technique and k-nearest neighbours
(KNNs) based classification technique.
