In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the...In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.展开更多
Recently,an increasing number of works start investigating the combination of fog computing and electronic health(ehealth)applications.However,there are still numerous unresolved issues worth to be explored.For instan...Recently,an increasing number of works start investigating the combination of fog computing and electronic health(ehealth)applications.However,there are still numerous unresolved issues worth to be explored.For instance,there is a lack of investigation on the disease prediction in fog environment and only limited studies show,how the Quality of Service(QoS)levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance(e.g.,accuracy and time efficiency).To address these issues,we propose a fog-based framework for disease prediction based on Medical sensor data streams,named FogMed.This framework aims to improve the disease prediction accuracy by achieving two objectives:QoS guarantee of fog services and anomaly prediction of Medical data streams.We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed.The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.展开更多
文摘In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.
基金This work is supported by NUIST Students’Platform for Innovation and Entrepreneurship Training Program,the National Natural Science Foundation of China(Grants No61702274)the Natural Science Foundation of Jiangsu Province(Grants No BK20170958),and PAPD.
文摘Recently,an increasing number of works start investigating the combination of fog computing and electronic health(ehealth)applications.However,there are still numerous unresolved issues worth to be explored.For instance,there is a lack of investigation on the disease prediction in fog environment and only limited studies show,how the Quality of Service(QoS)levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance(e.g.,accuracy and time efficiency).To address these issues,we propose a fog-based framework for disease prediction based on Medical sensor data streams,named FogMed.This framework aims to improve the disease prediction accuracy by achieving two objectives:QoS guarantee of fog services and anomaly prediction of Medical data streams.We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed.The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.
