DNS Traffic Forecasting Using Deep Neural Networks

Madariaga, Diego; Panza, Martín; Bustos-Jiménez, Javier

doi:10.1007/978-3-030-19945-6_12

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Moreover, the CNN variants simultaneously could capture the spatial and temporal features, for instance, more dilated causal CNN used in (Kuang et al, 2019), ResNet layers adopted in (Qiu et al, 2017). Furthermore, in passenger demand prediction (Madariaga et al, 2018), deep neural networks (DNNs) have shown good performance and deliver a strong learning facility due to their multiple hidden layers. Deep learning-based algorithms have made significant contributions to taxi demand forecasting.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Regional‐based multi‐module spatial–temporal networks predicting city‐wide taxi pickup/dropoff demand from origin to destination

et al. 2021

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Taxi demand forecasting from origin to destination (OD) is an important component in managing public transportation needs on a city-wide scale. Accurate taxi demand forecasting may provide several benefits, including economic and traffic flow optimization. However, due to complicated spatial-temporal connections and irregular distant locations, predicting taxi demand becomes difficult. To address these issues, we proposed a novel architecture of multi-module spatial-temporal networks to collectively predict city-wide OD taxi demand. In our work to deal with adjacent areas in a city, we employed the 3D convolutional neural networks to extract the spatialtemporal dependencies and learn the OD taxi demand pattern. To handle remote areas, we created an attention-based auto encoder-decoder, in which the input of the set of convolutional layers creates a feature matrix. The feature matrix embeds the spatial-temporal correlation jointly and passing through the encoder layer. We encode the spatial-temporal features with the help of pooling layer, then flatten layer used the back-propagation method to decode the weight matrix. We apply the normalization function to determine the demand pattern influences. The influence vector we compute with the Euclidean distance formula to determine the similarity of all distant regions. Finally, we use the attention mechanism to calculate the attention weight score for each region that its neighbour impacted. Then we used long shortterm memory, we captured the significant relationship of spatial-temporal dependencies with external factors. We train our model simultaneously to forecast city-wide taxi services OD. We have performed comprehensive experiments and large-scale dataset comparisons that reveal the taxi demand prediction problem.attention-based auto encoder-decoder, multi-module, origin-destination | INTRODUCTIONTaxis, being one of the most common modes of urban transportation, have had an influence on people's daily life. Due to the convenience it provides for our daily journeys, online cab riding services such as Didi Uber, NYCTaxi, and others have seen a remarkable development in recent years. This large business, on the other hand, it is all affected by certain unsustainable operations (Harrison et al., 2020;Qi & Li, 2020), that is, long

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Section: Deep Learning Methodsmentioning

confidence: 99%

Regional‐based multi‐module spatial–temporal networks predicting city‐wide taxi pickup/dropoff demand from origin to destination

et al. 2021

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“…The convolutional neural network (CNN) as the residual network captures the spatial features of the data, and the combination of LSTM as the RNN and the attention mechanism captures the temporal dependency. The analysis of LSTM, LSTM‐CNN, and weighted moving average as traffic forecasters of DNS servers were executed in Reference 34. A scheme using K‐means as a cluster of cells and LSTM was suggested by Santos et al 35 to predict Milan's mobile internet traffic, compared with the gated recurrent unit (GRU) network.…”

Section: Related Workmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

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With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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“…Recently, the temporal and spatial analysis of data traffic on the mobile network [16] has shown how different human patterns have different effects over the network state, generating distinct patterns of the data traffic in diverse locations. Also, as researches have shown how this periodicity is also present in DNS data [12], important conclusions about user behavior can be deduced at analyzing this portion of the network, in order to optimize the performance of this critical component of the Internet.…”

Section: Related Workmentioning

confidence: 99%

Revealing User Behavior by Analyzing DNS Traffic

Panza¹,

Madariaga²,

Bustos-Jiménez³

2020

Machine Learning for Networking

Self Cite

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The Domain Name System (DNS) is today a fundamental part of Internet's working. Considering that Internet has grown in the last decades as part of human's culture, user patterns regarding their behavior are present in the network data. As a consequence, some of these human behavior patterns are present as well in DNS data. With real data from the '.cl' ccTLD, this work seeks to detect those human patterns by using Machine Learning techniques. As DNS traffic is described by a time series, particular and complex techniques have to be used in order to process the data and extract this information. The procedure that we apply in order to achieve this goal is divided in two stages. The first one consists of using clustering to group DNS domains basing on the similarity between their users' activity. The second stage establishes a comparison between the obtained groups by using Association Rules. Finding human patterns in the data could be of high interest to researchers that analyze the human behavior regarding Internet's usage. The procedure was able to detect some trends and patterns in the data that are discussed along with proper evaluation measures for further comparison.

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DNS Traffic Forecasting Using Deep Neural Networks

Cited by 5 publications

References 19 publications

Regional‐based multi‐module spatial–temporal networks predicting city‐wide taxi pickup/dropoff demand from origin to destination

Regional‐based multi‐module spatial–temporal networks predicting city‐wide taxi pickup/dropoff demand from origin to destination

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Revealing User Behavior by Analyzing DNS Traffic

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