An Efficient Internet Traffic Classification System Using Deep Learning for IoT

作     者：Muhammad Basit Umair Zeshan Iqbal Muhammad Bilal Jamel Nebhen Tarik Adnan Almohamad Raja Majid Mehmood 

作者机构：Department of Computer ScienceUniversity of Engineering and TechnologyTaxila47050Pakistan Division of Computer and Electronics Systems EngineeringHankuk University of Foreign StudiesYongin-siKorea Electrical-Electronics Engineering DepartmentFaculty of EngineeringKarabük University78050KarabükTurkey Prince Sattam bin Abdulaziz UniversityCollege of Computer Engineering and SciencesAlkharj11942Saudi Arabia Information and Communication Technology DepartmentSchool of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：407-422页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work has supported by the Xiamen University Malaysia Research Fund(XMUMRF)(Grant No:XMUMRF/2019-C3/IECE/0007) 

主　　题：Deep learning internet traffic classification network traffic management QoS aware application classification 

摘      要：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 *** IoT devices are connected to a network therefore prone to *** management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic *** 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 ***,machine learning-based models incline to misclassify internet traffic due to improper feature *** this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet *** examine the performance of the proposed technique,Moore-dataset is used for training 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 *** 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.