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.

中文翻译：

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使用深度学习的物联网高效互联网流量分类系统

物联网 (IoT) 定义了连接到互联网并在彼此和中心位置之间共享大量数据的设备网络。这些物联网设备连接到网络，因此容易受到攻击。各种管理任务和网络操作（例如安全、入侵检测、服务质量配置、性能监控、资源配置和流量工程）都需要流量分类。由于传统分类方案（例如基于端口和基于有效载荷的方法）的无效性，研究人员提出了基于浅层神经网络的基于机器学习的流量分类系统。此外，由于特征选择不当，基于机器学习的模型倾向于错误分类互联网流量。在这项研究中，提出了一种高效的基于多层深度学习的分类系统，以克服这些可以对互联网流量进行分类的挑战。为了检查所提出技术的性能，使用摩尔数据集来训练分类器。所提出的方案采用预处理数据并使用深度神经网络（DNN）提取流特征。特别是，最大熵分类器用于对互联网流量进行分类。实验结果表明，所提出的混合深度学习算法是有效的，对互联网流量分类的准确率达到了99.23%。此外，与基于支持向量机 (SVM) 的分类技术和基于 k 最近邻 (KNN) 的分类技术相比，所提出的算法实现了最高的准确度。