Open Access

ARTICLE

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

by Muhammad Basit Umair¹, Zeshan Iqbal¹, Muhammad Bilal², Jamel Nebhen⁴, Tarik Adnan Almohamad³, Raja Majid Mehmood^5,*

1 Department of Computer Science, University of Engineering and Technology, Taxila, 47050, Pakistan
2 Division of Computer and Electronics Systems Engineering, Hankuk University of Foreign Studies, Yongin-si, Korea
3 Electrical-Electronics Engineering Department, Faculty of Engineering, Karabük University, 78050, Karabük, Turkey
4 Prince Sattam bin Abdulaziz University, College of Computer Engineering and Sciences, Alkharj, 11942, Saudi Arabia
5 Information and Communication Technology Department, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, 43900, Malaysia

* Corresponding Author: Raja Majid Mehmood. Email:

(This article belongs to the Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)

Computers, Materials & Continua 2022, 71(1), 407-422. https://doi.org/10.32604/cmc.2022.020727

Received 04 June 2021; Accepted 05 July 2021; Issue published 03 November 2021

Abstract

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.

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Keywords

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