… failed to capture the pattern diversity of different city functional zones and the traffic … -based
accurate traffic prediction in cellular networks under the scenario of crossdomain big data. In …

… The work presented in [27] by Rezaei et al. surveyed DL models for encrypted traffic
classification. This paper addressed different DL-based classification models for network traffic …

… is in the model taxonomy; their work categorized the models based on … model used. We will
now discuss the different types of prediction models that have been used for traffic prediction …

… a traffic prediction method for one whole day using a deep neural network based on historical
traffic flow data … This paper focuses on forecasting traffic flow data one whole day into the …

… We propose a traffic flow prediction model with spatial-temporal attention (STANN),
which exploits both temporal and spatial correlations in traffic using the attention mechanism. …

… network traffic data, we propose a novel Difficult Set Sampling Technique(DSSTE) algorithm
to tackle the class imbalance problem in network traffic. … intrusion detection model based on …

… EKFs [8] and the probabilistic heterogeneous traffic data fusion based EKFs [9], etc. … was
used for freeway network estimation problems [12], [13]. The forecasting models based on the …

… based method to classify P2P network traffic. Their private collected data (including VPN and
non -VPN network traffic… % of the whole dataset and the solution provided low performance. …

… based spatio-temporal traffic forecasting network, named ST-3DNet, to predict future traffic
data. … -fold: • We introduce 3D convolutions into traffic prediction domain. ST-3DNet adopts 3D …

… A dynamic model based on the k-nearest neighbour non-parametric … learning models for
traffic flow prediction, we propose a novel hybrid model integrating CNN and Bi-LSTM networks …