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...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.展开更多
Cost-sensitive learning has been applied to resolve the multi-class imbalance problem in Internet traffic classification and it has achieved considerable results. But the classification performance on the minority cla...Cost-sensitive learning has been applied to resolve the multi-class imbalance problem in Internet traffic classification and it has achieved considerable results. But the classification performance on the minority classes with a few bytes is still unhopeful because the existing research only focuses on the classes with a large amount of bytes. Therefore, the class-dependent misclassification cost is studied. Firstly, the flow rate based cost matrix （FCM） is investigated. Secondly, a new cost matrix named weighted cost matrix （WCM） is proposed, which calculates a reasonable weight for each cost of FCM by regarding the data imbalance degree and classification accuracy of each class. It is able to further improve the classification performance on the difficult minority class （the class with more flows but worse classification accuracy）. Experimental results on twelve real traffic datasets show that FCM and WCM obtain more than 92% flow g-mean and 80% byte g-mean on average; on the test set collected one year later, WCM outperforms FCM in terms of stability.展开更多
基金This work has supported by the Xiamen University Malaysia Research Fund(XMUMRF)(Grant No:XMUMRF/2019-C3/IECE/0007)。
文摘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.
基金supported by the National Basic Research Program of China(2007CB307100,2007CB307106)
文摘Cost-sensitive learning has been applied to resolve the multi-class imbalance problem in Internet traffic classification and it has achieved considerable results. But the classification performance on the minority classes with a few bytes is still unhopeful because the existing research only focuses on the classes with a large amount of bytes. Therefore, the class-dependent misclassification cost is studied. Firstly, the flow rate based cost matrix （FCM） is investigated. Secondly, a new cost matrix named weighted cost matrix （WCM） is proposed, which calculates a reasonable weight for each cost of FCM by regarding the data imbalance degree and classification accuracy of each class. It is able to further improve the classification performance on the difficult minority class （the class with more flows but worse classification accuracy）. Experimental results on twelve real traffic datasets show that FCM and WCM obtain more than 92% flow g-mean and 80% byte g-mean on average; on the test set collected one year later, WCM outperforms FCM in terms of stability.
