Network traffic prediction dataset. we first create a large-scale and comprehensive network traffic benchmark from 7 distinct datasets with 20 tasks. This study focuses on the tasks of traffic estimation and prediction and presents an up-to-date collection of available datasets and tools, as a reference for those who seek public resources. Recently, a significant amount of research efforts Must classify images of traffic signs captured by cameras # # STAGE 1 GOAL: # Before building a full image model, implement a Perceptron-based neural # network to understand how neural networks make decisions. Apr 3, 2023 · The OPNET dataset: It contains network traffic data on 120 nodes within 90 days, is generated by the OPNET network simulation software. . Aiming to address the issues of noise interference in traffic data and the high complexity of deep learning models, this paper proposes a lightweight wavelet-enhanced Transformer model with stacked denoising for traffic speed prediction. # # The perceptron will predict whether a traffic sign means "STOP" or "NOT STOP" # using extracted features from the sign. About Dataset elcome to the cutting-edge world of traffic prediction! This Kaggle dataset is a goldmine for data enthusiasts, machine learning aficionados, and urban planning visionaries. Mar 20, 2025 · Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. Foreseeing Congestion using LSTM on Urban Traffic Flow Clusters ICSAI 2019 Keras; dataset: CityPulse Using LSTM and GRU neural network methods for traffic flow prediction IEEE YAC 2016 Keras; dataset: PeMS but different from everyone else A Dynamic Traffic Awareness System for Urban Driving IEEE 4 days ago · This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. Mar 15, 2026 · Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow and evaluates the framework on two real-world large scale road network traffic datasets and observes consistent improvement. The dataset, provided by Jun 17, 2021 · The goal is to predict the traffic volume 15 minutes into the future for all sensor locations. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons. Mar 15, 2024 · Comprehensive Network Traffic Benchmark. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. Use this Dataset for analysis the network traffic and designing the applications Feb 24, 2026 · List of datasets related to networking. Jun 6, 2023 · 5G network operators need data traffic predictions to plan network expansion schemes. Hybrid Fusion Intrusion Detection using Random Forest and Dense Autoencoder on UNSW-NB15 dataset - gbkeku/hybrid-intrusion-detection-unsw Hence, it deserves to explore an efficient data-driven traffic prediction framework that can adapt to different network tasks. This article first examines the general forms and characteristics of various network traffic based on specific datasets. Hence, current traditional models cannot forecast network traffic that acts as a nonlinear system Contribute to UJK9/ANALYSIS-OF-TRAFFIC-BEHAVIOR-USING-TRAFFIC-PREDICTION-DATASET development by creating an account on GitHub. This paper proposes a transfer learning strategy based on graph convolution neural network to achieve the task of large-scale traffic prediction. This study proposes a deep-learning-based flow prediction method, established using a residual neural network (ResNet) for regression. Traffic prediction is the task of predicting future traffic measurements (e. Yuguang Yang and colleagues demonstrate performance improvement over state-of-the-art forecasting tools of a Sep 3, 2020 · Currently, the Google Maps traffic prediction system consists of the following components: (1) a route analyser that processes terabytes of traffic information to construct Supersegments and (2) a novel Graph Neural Network model, which is optimised with multiple objectives and predicts the travel time for each Supersegment. As a result, the resource management is becoming more difficult and more complex for Internet service providers. Apr 1, 2024 · Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. LSTM and ARIMA for network traffic prediction (Christoph Kaiser's MA) - CN-UPB/ml-traffic-prediction A novel dataset tailored for ML applications for IoT network security Jan 1, 2021 · Network traffic matrix prediction is a methodology of predicting network traffic behavior ahead of time in order to improve network management and planning. Given their proficiency in extracting image features we use three pre-trained models: the AlexNet, the ResNet combined with CNN, and the Apr 23, 2025 · These datasets feature traffic flow information from California, USA, and serve as standard benchmarks for network traffic prediction research. To tackle these issues, we introduce a Hybrid CNN-GRU-LSTM model—an advanced deep learning framework that Jan 16, 2022 · Traffic is everywhere, on roads, highways, rail networks, and in pedestrian zones. volume, speed, etc. A nationwide cellular mobile network contains tens of thousands of base stations. In order to increase predictive performance, this study introduces an upgraded Adaptive Machine Learning-based Cellular Traffic Prediction (AML-CTP) framework that incorporates cutting-edge machine learning Complete Traffic: By having a user profiling agent and 12 different machines in Victim-Network and real attacks from the Attack-Network. This study evaluates state-of-the-art deep learning models on CESNET-TimeSeries24, a recently published, comprehensive real-world network traffic dataset from the ISP network CESNET3 spanning multivariate time series over 40 weeks. In all cases, we provide a complete and self-contained presentation of the Mar 16, 2026 · The system provides test datasets in the data/ directory for model evaluation. Deep learning algorithms help to infer network topology, find traffic bottlenecks, solve the multiobjective location inventory problem, construct data reduction algorithms, and predict short-term traffic flow under heterogeneous conditions. The method uses density-based clustering to oversample minority classes based on local cluster density, leading to higher F1 scores and better classification accuracy compared to traditional resampling methods. Traffic-Congestion-Prediction-Feature-Engineering-and-LightGBM The dataset for this competition includes aggregate stopped vehicle information and intersection wait times. The model is implemented using TensorFlow and Keras. These datasets are derived from the NSL-KDD test set and contain network traffic records labeled with attack types. Apr 15, 2025 · Effective traffic prediction is crucial for optimizing urban transportation systems, minimizing congestion, and enhancing overall efficiency. May 27, 2023 · Network traffic prediction (NTP) can predict future traffic leveraging historical data, which serves as proactive methods for network resource planning, allocation, and management. In this treasure trove of information, you'll find a comprehensive collection of real-world traffic data meticulously curated for your analytical prowess. Apr 13, 2025 · AI Quick Summary This study introduces an adaptive cluster-based synthetic minority oversampling technique to improve traffic mode choice prediction in imbalanced datasets. Jan 1, 2023 · Abstract and Figures This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. The PeMSD4 dataset consists of data from sensors along Highway 4, whereas the PeMSD8 dataset includes data from sensors on Highway 8. Network Traffic Prediction (NTP) aims to forecast the total amount of traffic expected based on historical data to avoid future congestion and maintain high network quality [64]. Flexible Data Ingestion. Dataset is captured in an intelligent platform built using Oculus Quest 2, traffic manager, and cloud rendering device using Virtual Desktop Streamer. Useful resources for traffic prediction, including popular papers, datasets, tutorials, toolkits, and other helpful repositories. - Coolgiserz/Awesome-Traffic-Prediction 🚀 network-intrusion-detection-ml - Protect Your Network with AI 📋 Description This application is a Machine Learning-based Intrusion Detection System that uses the NSL-KDD dataset. The task can be broken down into two main parts: Network Traffic Prediction Dataset The data was captured in Europe - dataset Euro28 and in USA - dataset US26 in optical network infrastracture and in the next step was generated using original one. Accurate cellular traffic prediction is crucial for capacity management, energy efficiency, and Aug 22, 2025 · However, existing prediction methods fail to fully capture the periodicity and spatial heterogeneity of the data. Several surveys already exist that review and classify the many works on network trafic forecasting based on different approaches, from statistical models to artificial neural The SO-TAD dataset comprises 2,186 samples, including 282 traffic accident instances, each providing spatio-temporal localization information. We construct our OPNET dataset using the existing node model in OPNET, and the network traffic volume is recorded at 10-minute intervals. A TM quantifies traffic demand between source-destination (SD) node pairs in a network, and are essential for network planning, serving as critical input for many traffic engineering (TE) tasks, such as routing optimization and network resource allocation [19]. Apr 1, 2022 · Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). About Dataset Context Traffic congestion is rising in cities around the world. In our evaluation, we use a real-world dataset to compare HiSTM against several baselines, showing a 29. However, applying GNNs to the accident pre-diction problem is made challenging by a lack of suitable graph-structured trafic accident prediction datasets. md Feb 26, 2025 · We demonstrate the usage of the dataset’s time series for network traffic forecasting to validate the usability of the dataset. Feel free to comment with updates. In this paper, we review existing network classification Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Useful for data-driven evaluation or machine learning approaches. Feb 10, 2025 · Network Traffic Prediction (NTP) aims to forecast the total amount of traffic expected based on historical data to avoid future congestion and maintain high network quality [64]. Discover what actually works in AI. Jan 1, 2022 · This paper proposed a LSTM-XGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity, stationary and abnormality of time series. Predicting network traffic is crucial for efficient resource management in 5G networks. Simulated Data for Enhancing Cybersecurity Models and Intrusion Detection System Location-based hourly traffic congestion levels for model training. Sep 27, 2024 · Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. The data used for this work was captured in an office environment by a company from the field of network security and Jun 1, 2024 · 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. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. However, TMs are challenging to measure and collect in real time [19], motivating the need for accurate TM prediction to support TE The wide-spread availability of Internet flow data allows machine learning algorithms to learn the complex relationships in network traffic and form models capable of forecasting traffic flows. Jan 27, 2022 · We explore which algorithms help accurately predict road traffic and what are the main approaches to congestion forecasting and route planning. Real-Time Network Traffic Volume Prediction using time series and recurrent neural network - Network-Traffic-Prediction/data at master · SaifNOUMA/Network-Traffic-Prediction Nov 23, 2023 · Before prediction, the deep Autoencoder model helps to remove anomaly data and train the traffic model upon the normal traffic dataset by setting the batch size to 256, epochs 10 using fivefold We train and compare four machine learning models, one fully connected neural network and three graph neural networks. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Jun 17, 2021 · The goal is to predict the traffic volume 15 minutes into the future for all sensor locations. With the rapid growth of Internet Technology, network traffic is growing exponentially. ) in a road network (graph), using historical data (timeseries). 4% MAE improvement over the STN baseline while using 94% fewer parameters. Besides, NTP can also be applied for load generation in simulated and emulated as well as digital twin networks (DTNs). Accurately predicting metrics such as bandwidth utilization in future networks can assist service providers in predicting network congestion, allowing for proactive network expansion, adjustments, and optimization. We construct two datasets with different temporal granularity, namely, minute and hour. Jul 29, 2025 · Metaverse Network Traffic dataset consists of comprehensive applications from Virtual, Augmented, and Mixed Realities. Mar 16, 2026 · Making Predictions Relevant source files Purpose and Scope This document describes how to use the trained Random Forest model to make threat predictions on network traffic data. We select the time series with IP address ID 103, the number of IP Jul 1, 2024 · This work explores two approaches for converting the time series data to images by allowing more precise feature extraction and then performing traffic prediction on an image dataset, thus increasing accuracy. With a given road network, we know the spatial connectivity between sensor locations. This project implements predictive models using neural networks to analyze historical traffic data and provide accurate short-term and long-term traffic predictions for intelligent transportation systems. Therefore, the prediction accuracy is limited, especially in long-term prediction. Our Feb 1, 2024 · To address the different reliance on long/short-term datasets for various network traffic prediction scenarios. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. - networking_datasets. Furthermore, within the proposed frame prediction-based traffic accident detection framework, we designed a three-channel scene feature fusion generative adversarial network (TCFF-GAN). Mar 8, 2024 · To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. Network trafic prediction is in fact a largely explored subject in networking, with early works dating back to the seventies and a flurry of recent proposals fostered by the success of machine and deep learning tools. Particularly, they contain statistics of the per-source/destination pair packet-level delay, jitter and losses. Our datasets include samples with different input topologies, routing configurations and traffic patterns, and each sample contains accurate measurements of relevant end-to-end key performance metrics resulting from the simulation. It enables the network operator to present resource-allocation strategies and, in turn, optimizes network resources dynamically. May 7, 2025 · The scheme involves the collection of a large real Internet traffic dataset including encrypted and non-encrypted traffic through sensors deployed on real-world network access equipment. Network traffic analysis and prediction is a proactive approach to ensure secure, reliable and qualitative network communication. This example shows how to forecast traffic condition using graph neural networks and LSTM. Traffic congestion results in prolonged travel durations, higher fuel consumption, economic setbacks, and increased environmental pollution. Abstract—Traffic prediction plays an essential role in intelli-gent transportation system. Nov 13, 2021 · Representative datasets: Shortage of public and representative datasets to train ML models is a fundamental challenge network-related prediction scenarios. Polynomials of moving and rest queues with defined traffic characteristics fuel flow mechanisms. Our benchmark covers diverse data types and a wide range of tasks, which provides fair comparisons of existing machine learning models in both traffic classification and generation. To address the challenges, an Embedded Attention Spatio-Temporal Graph Convolutional Networks traffic flow prediction model (Embedded Attention Spatio-Temporal Graph Convolutional Networks, EA-STGCN) is proposed. The task of predicting future traffic congestion based on historical and live data is highly relevant to everyone An advanced traffic flow prediction system leveraging deep learning techniques to forecast traffic patterns and optimize transportation management. Feb 10, 2021 · Traffic prediction plays an essential role in intelligent transportation system. Contribute to westermo/network-traffic-dataset development by creating an account on GitHub. Abstract: For modern networks to maximise Quality of Service (QoS), precise cellular traffic prediction is crucial, particularly given the increasing need for real-time applications. PEMS08: The PEMS08 dataset contains traffic data from a highway network spanning from July 1, 2016, to July 31, 2016. 3 days ago · The traffic flow micro-dynamics integration with Queuing Theory and the use of historical/ real-time multimodal data such as sensor feeds, GPS trajectories, and incident reports as a basis for forecasting helps in robust congestion prediction. Feb 26, 2024 · Extensive experiments on real-world datasets demonstrate our method’s superiority over current approaches, showcasing its potential for network traffic prediction and accurate forecasting. It helps in identifying and preventing network intrusions. It is designed to assist researchers and practitioners in developing machine learning models, particularly for traffic volume forecasting and bandwidth utilization. Labelled Dataset: Section 4 and Table 2 show the benign and attack labels for each day. Feb 10, 2025 · They can act in the background to analyze and predict traffic conditions more accurately than ever and help to optimize the design and management of network services. Specifically, we are interested in predicting the future values of the traffic speed given a history of the traffic speed for a collection of road segments. Recently, a significant amount of research effort has been devoted to this area, greatly advancing network traffic prediction (NTP) abilities. Dec 29, 2025 · Recent studies have highlighted that network traffic may be influenced by various external factors such as weather conditions and user behavior, making it challenging to achieve precise predictions using only historical traffic data. Forecasting-Mobile-Network-Traffic Overview This is a task that is focused on analyzing and forecasting future traffic from mobile data traffic dataset recorded in a real-world network that covers a large city of Milan & Province of Trentino, covering the period of November to December 2013. Jan 5, 2022 · Intelligent cellular traffic prediction is very important for mobile operators to achieve resource scheduling and allocation. One popular method to solve this problem is to consider each road segment's traffic speed as a separate timeseries and predict the future values of Accurately predicting metrics such as bandwidth utilization in future networks can assist service providers in predicting network congestion, allowing for proactive network expansion, adjustments, and optimization. This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. The impacts are significant. About Dataset This dataset is a preprocessed version tailored for network traffic prediction and analysis tasks. Custom Testbed Deployment In our project, we build an SDN network testbed using Mininet simulator to evaluate our SDN TM measurement application and provide a second dataset for prediction task. In reality, people often need to predict very large scale of cellular traffic involving thousands of cells. To adapt to the ever-changing network environment and requirements, methods for network traffic prediction have evolved from traditional statistical models to gradually incorporate Jul 21, 2020 · Intelligent Hybrid model to Enhance Time Series Models for Predicting Network Traffic, the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models. Aug 7, 2025 · HiSTM employs selective state space methods to capture spatial and temporal patterns in network traffic. 1 day ago · Hybrid Fusion Intrusion Detection using Random Forest and Dense Autoencoder on UNSW-NB15 dataset - gbkeku/hybrid-intrusion-detection-unsw Sep 1, 2025 · The decomposability of cellular network traffic and its spatiotemporal correlations were leveraged by DISTGCN to enhance prediction accuracy. g. This paper focuses on accurately predicting background traffic matrix (TM) of typical local To allow us to incorporate road network information, graph-based approaches such as Graph Neural Networks (GNNs) are a natural choice. The system supports two prediction modes: local batch predictions for offline analysis and API-based predictions for real-time inference. The Westermo network traffic dataset. To adapt to the ever-changing network environment and requirements, methods for network traffic prediction have evolved from traditional statistical models to gradually incorporate Mar 21, 2024 · The aim of this work is to make time series predictions for real network traffic data by using long short-term memory neural networks (LSTMs). This project uses a synthetic dataset to simulate 5G network traffic and trains an LSTM model to predict future traffic volumes. In addition, we construct a large dataset containing text-traffic pairs for the TTG task. Recently, a significant amount of research efforts have been Dec 17, 2024 · Network traffic prediction is crucial for optimizing network performance, especially in high-demand IT networks that require real-time decision-making. One popular method to solve this problem is to consider each road segment's traffic speed as a separate timeseries and predict the Dec 27, 2021 · In our paper, we review some of the latest works in deep learning for traffic flow prediction. A hybrid model based on Long Short-Term Memory and Transformer is proposed to predict network traffic using real operational Key Performance Indicators (KPI) data from 23 cells in Bandung over four months, providing a strong basis for the application of traffic prediction in operational scenarios. Contributing factors include expanding urban populations, aging infrastructure, inefficient and uncoordinated traffic signal timing and a lack of real-time data. In this paper Dec 28, 2021 · Introduction This example shows how to forecast traffic condition using graph neural networks and LSTM. The objective is to predict traffic volumes for all traffic stations (nodes) for the next hour given the current traffic volumes, month, weekday and hour. Jan 7, 2025 · Similar to the HZMetro dataset, it documents the total count of individuals entering and exiting each station within these 15-minute intervals. 89 Due to the data-driven nature of ML solutions, lack of proper representative data or poor quality can lead to reduced accuracy. Oct 30, 2022 · This paper proposed a deep learning-based network traffic prediction model, which can capture the characteristics of network traffic information changes by inputting past network traffic data to achieve the effect of future network traffic prediction. Mar 9, 2016 · Traffic and congestion prediction on LTE networks Overview To handle the dramatic increase in data volume and better serve their customers, mobile network operators need to design and manage network architectures according to the required demand. Aggregated at 5-minute intervals, it comprises 288 intervals per day. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. One of the primary approaches to anomaly detection are methods based on forecasting. 🛡️ AI-Based Network Intrusion Detection System (NIDS) A production-ready machine learning system for detecting network intrusions in real-time, trained on the NSL-KDD dataset using a RandomForest classifier. Nevertheless, extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing performance overestimation of anomaly Sep 27, 2024 · Nevertheless, extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing performance overestimation of anomaly detection algorithms. Various techniques are proposed and experimented for analyzing network traffic including neural network based techniques to data mining techniques. rntxewkn seot jmeb cxb faeit dai hqwjxa hyjdglm qhl mhtsv