Network traffic classification:Techniques,datasets,and challenges

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.

Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis.The results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic.The high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of death.The results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the COVID-19.It can be suggested that social media posts may affect human psychology and behavior.These results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.

Modeling CO_(2) Emission of Middle Eastern Countries Using Intelligent Methods

CO_(2) emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities.Consequently,these factors must be considered for CO_(2) emission prediction for seven middle eastern countries including Iran,Kuwait,United Arab Emirates,Turkey,Saudi Arabia,Iraq and Qatar.In order to propose a predictive model,a Multilayer Perceptron Artificial Neural Network(MLP ANN)is applied.Three transfer functions including logsig,tansig and radial basis functions are utilized in the hidden layer of the network.Moreover,various numbers of neurons are applied in the structure of the models.It is revealed that using MLP ANN makes it possible to accurately predict CO_(2) emission of these countries.In addition,it is concluded that using logsig transfer function leads to the highest accuracy with minimum value of mean squared error(MSE)which is followed by the networks with radial basis and tansig transfer functions.The R-squared of the networks with logsig,radial basis and tansig transfer functions are 0.9998,0.9997 and 0.9996,respectively.Finally,comparison of the proposed model with a similar study,considered five countries in the same region,reveals higher accuracy in term of MSE.