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16 results for “traffic flow”

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zenodo48/100

Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks

The publication titled "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks" is supported by the STRIDE K3 project. The dataset used in the publication is uploaded here.

opencc-by-4.0Dec 2023View details →

zenodo44/100

Network traffic datasets created by Single Flow Time Series Analysis

Network traffic datasets created by Single Flow Time Series Analysis Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomá&scaron; Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:   <blockquote> J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876. </blockquote> This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf   In the following table is a description of each dataset file: <table> <tbody> <tr> <td>File name</td> <td>Detection problem</td> <td>Citation of original raw dataset</td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv</td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td> Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020  </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.</td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.</td> </tr> <tr> <td>edge_iiot_multiclass.csv</td> <td>Multi-class classification of IoT malware</td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.</td> </tr> <tr> <td>https_brute_force.csv</td> <td>Binary detection of HTTPS Brute Force</td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020</td> </tr> <tr> <td>ids_cic_binary.csv</td> <td>Binary detection of intrusion in IDS</td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.</td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_unsw_nb_15_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.</td> </tr> <tr> <td>ids_unsw_nb_15_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.</td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23</td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021</td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021</td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022</td> </tr> <tr> <td>vpn_vnat_multiclass.csv</td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022</td> </tr> </tbody> </table>

opencc-by-4.0Jun 2023View details →

zenodo44/100

Network traffic datasets with novel extended IP flow called NetTiSA flow

Network traffic datasets with novel extended IP flow called NetTiSA flow Datasets were created for the paper: NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification -- Josef Koumar, Karel Hynek, Jaroslav Pe&scaron;ek, Tomá&scaron; Čejka -- which is published in The International Journal of Computer and Telecommunications Networking <a href="https://doi.org/10.1016/j.comnet.2023.110147" rel="nofollow">https://doi.org/10.1016/j.comnet.2023.110147</a> Please cite the usage of our datasets as: <blockquote> Josef Koumar, Karel Hynek, Jaroslav Pe&scaron;ek, Tomá&scaron; Čejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification", Computer Networks, Volume 240, 2024, 110147, ISSN 1389-1286 <pre><code>@article{KOUMAR2024110147, title = {NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification}, journal = {Computer Networks}, volume = {240}, pages = {110147}, year = {2024}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2023.110147}, url = {https://www.sciencedirect.com/science/article/pii/S1389128623005923}, author = {Josef Koumar and Karel Hynek and Jaroslav Pe&scaron;ek and Tomá&scaron; Čejka} } </code></pre> </blockquote> This Zenodo repository contains 23 datasets created from 15 well-known published datasets, which are cited in the table below. Each dataset contains the NetTiSA flow feature vector.   NetTiSA flow feature vector The novel extended IP flow called NetTiSA (Network Time Series Analysed) flow contains a universal bandwidth-constrained feature vector consisting of 20 features. We divide the NetTiSA flow classification features into three groups by computation. The first group of features is based on classical bidirectional flow information---a number of transferred bytes, and packets.  The second group contains statistical and time-based features calculated using the time-series analysis of the packet sequences. The third type of features can be computed from the previous groups (i.e., on the flow collector) and improve the classification performance without any impact on the telemetry bandwidth.   Flow features The flow features are: <ul> <li>Packets is the number of packets in the direction from the source to the destination IP address.</li> <li>Packets in reverse order is the number of packets in the direction from the destination to the source IP address.</li> <li>Bytes is the size of the payload in bytes transferred in the direction from the source to the destination IP address.</li> <li>Bytes in reverse order is the size of the payload in bytes transferred in the direction from the destination to the source IP address.</li> </ul>   Statistical and Time-based features The features that are exported in the extended part of the flow. All of them can be computed (exactly or in approximative) by stream-wise computation, which is necessary for keeping memory requirements low. The second type of feature set contains the following features: <ul> <li>Mean represents mean of the payload lengths of packets</li> <li>Min is the minimal value from payload lengths of all packets in a flow</li> <li>Max is the maximum value from payload lengths of all packets in a flow</li> <li>Standard deviation is a measure of the variation of payload lengths from the mean payload length</li> <li>Root mean square is the measure of the magnitude of payload lengths of packets</li> <li>Average dispersion is the average absolute difference between each payload length of the packet and the mean value</li> <li>Kurtosis is the measure describing the extent to which the tails of a distribution differ from the tails of a normal distribution</li> <li>Mean of relative times is the mean of the relative times which is a sequence defined as \(st = \{t_1 - t_1, t_2 - t_1, ..., t_n - t_1\} \)</li> <li>Mean of time differences is the mean of the time differences which is a sequence defined as \(dt = \{ t_j - t_i | j = i + 1, i \in \{1, 2, \dots, n - 1\} \}.\)</li> <li>Min from time differences is the minimal value from all time differences, i.e., min space between packets.</li> <li>Max from time differences is the maximum value from all time differences, i.e., max space between packets.</li> <li>Time distribution describes the deviation of time differences between individual packets within the time series. The feature is computed by the following equation: \(tdist = \frac{ \frac{1}{n-1} \sum_{i=1}^{n-1} \left| \mu_{\{dt_{n-1}\}} - dt_i \right| }{ \frac{1}{2} \left(max\left(\{dt_{n-1}\}\right) - min\left(\{dt_{n-1}\}\right) \right) }\)</li> <li>Switching ratio represents a value change ratio (switching) between payload lengths. The switching ratio is computed by equation: \(sr = \frac{s_n}{\frac{1}{2} (n - 1)}\)</li> </ul>            where \(s_n\) is number of switches.    Features computed at the collector The third set contains features that are computed from the previous two groups prior to classification. Therefore, they do not influence the network telemetry size and their computation does not put additional load to resource-constrained flow monitoring probes. The NetTiSA flow combined with this feature set is called the Enhanced NetTiSA flow and contains the following features: <ul> <li>Max minus min  is the difference between minimum and maximum payload lengths</li> <li>Percent deviation is the dispersion of the average absolute difference to the mean value</li> <li>Variance is the spread measure of the data from its mean</li> <li>Burstiness is the degree of peakedness in the central part of the distribution</li> <li>Coefficient of variation is a dimensionless quantity that compares the dispersion of a time series to its mean value and is often used to compare the variability of different time series that have different units of measurement</li> <li>Directions describe a percentage ratio of packet direction computed as \(\frac{d_1}{ d_1 + d_0}\), where \(d_1\) is a number of packets in a direction from source to destination IP address and \(d_0\) the opposite direction. Both  \(d_1\) and \(d_0\) are inside the classical bidirectional flow.</li> <li>Duration is the duration of the flow</li> </ul>   The NetTiSA flow is implemented into IP flow exporter <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a>.   Description of dataset files In the following table is a description of each dataset file: <table> <tbody> <tr> <td> File name </td> <td> Detection problem </td> <td> Citation of the original raw dataset </td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv </td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td>Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. </td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>edge_iiot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>https_brute_force.csv </td> <td>Binary detection of HTTPS Brute Force </td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 </td> </tr> <tr> <td>ids_cic_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>unsw_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>unsw_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 </td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> <tr> <td>vpn_vnat_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2023View details →

zenodo40/100

Intersection Vehicular Traffic Flow Scheduling Datasets

Background Vehicular traffic congestion remains a major problem in most modern cities of the world. Therefore, efforts to minimize these congestions and their accompanying effects continuously receive much attention from researchers, traffic engineers, policymakers, etc. One important element required in the efforts of traffic engineering for the minimization of traffic congestion is data. Data influence the modeling and deployment of traffic scheduling systems. Most importantly, the objectives of modeling and deploying the traffic scheduling system influence the nature of the data to be used. Aim The aim of this study was to generate and provide a dataset that may be used in the training of computationally intelligent systems having basic objects of minimizing waiting time, travel time, etc. at various intersections (isolated intersections or roundabouts). Methodology The dataset (.csv) consisted of waiting time (W), queue length (Q), and phase duration (P). The waiting time is the time duration vehicles have waited at an intersection/roundabout before being scheduled to utilize the intersection/roundabout. The queue length refers to the number of vehicles (vehicular count) waiting at an intersection/roundabout. Phase duration is the time period a given vehicular flow (lane) is assigned the green wave to utilize the intersection/roundabout. The dataset was obtained through repeated training, testing, and modification of phase duration and the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The dataset considered bounded conditions on the three parameters (W, Q, P). The W and Q were bounded between zero and ninety – [0, 90] and the P is [13, 50]. That is, when W and Q are greater than or equal to the upper bound, the upper bound is used. Every flow may be assigned a minimum P of 13s and a maximum of 50s. The P-bounds assumed that the lower bound is large enough for vehicles on the assigned traffic flow to move to the safe region of the intersection before the scheduling system switches assignment to another traffic flow.  Conclusion The dataset may be used as a benchmark dataset for the improvement of traffic flow controllers as well as other datasets.

opencc-by-4.0Sep 2022View details →

zenodo36/100

NDVI, nocturnal road traffic noise and traffic flow over the canton of Geneva

This dataset relates the spatial distribution of several environmental variables (including NDVI and nocturnal road traffic noise) over the territory of the canton of Geneva (Switzerland) with the aim of studying the potential attenuation effect of vegetation on road traffic noise. Traffic flow measurement points are in this regard also included. The dataset contains two distinct group of files. The first one (hec_grid_ge) contains the hectometric vector grid discretization of the canton of Geneva excluding Lake Geneva. Each cell is characterized by the mean, median and standard deviation of several environmental variables (NDVI, nocturnal road traffic noise, daily road traffic noise and land surface temperature). In addition, BiLISA local Moran's I, p-value and spatial association type for the spatial correlation of median NDVI and spatial lag of median nocturnal road traffic noise are defined for each cell. The second group of files (traffic_ge) contains the traffic flow measurements points which are characterized with "hec_grid_ge" layer variables of the hectometric cell to which they belong to.

opencc-by-4.0Nov 2021View details →

zenodo36/100

Traffic flow formation in termites

The material shared here is part of the research project on the mechanism of traffic flow formation. We use termites as a biological model and their natural foraging activity as a method. Here, we present some video fragments extracted from the recordings of the full foraging process of the termites Constrictotermes cyphergaster at laboratory conditions. This research project is supported by the Brazilian Government through the Minas Gerais State Agency for Research (FAPEMIG), the Brazilian Council for Research (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES). Details of the videos: Each file recording is named with the ID of the nest used for each test. A test consisted of the passage of the termites from the box containing the entire nest to the foraging box through a bridge. The width and the form of the bridge were modified between tests. In the fragment, 2018-IX-07-RCB-N09 we tested a bridge with a constant width of 7.5 cm, while in the fragment 2018-IX-07-RCB-N18 we tested a "bottleneck" bridge with a hybrid width of 2.5 cm - 1.5 cm - 2.5cm. For aditional information, please contact us: Julieth Castiblanco (castiblancoq.j@gmail.com), Og DeSouza (og.souza@ufv.br).

opencc-by-4.0May 2019View details →

dryad36/100

IoT network traffic dataset using the custom flow representation

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publicNov 2025View details →

zenodo32/100

The dataset of "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow"

There are the datasets of the research named "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow".

opencc-by-4.0Sep 2022View details →

zenodo32/100

Learn from Human-driving Accidents to Attack Autonomous Driving_Practical Traffic Flow Attacks on Decision-making

We provide some demo videos of attack patterns

opencc-by-4.0May 2024View details →

dryad32/100

UNSW IoT traffic data with packets, flows, and protocols

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publicAug 2025View details →

zenodo28/100

EXPERIMENTAL STUDY OF THE INTENSITY AND COMPOSITION OF TRAFFIC FLOW AT INTERSECTIONS IN THE TERRITORY OF THE CITY OF NUKUS

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opencc-by-4.0Apr 2024View details →

dryad28/100

Automated vehicles and central business district parking: the effects of drop-off-travel on traffic flow and vehicle emissions

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publicMar 2020View details →

zenodo24/100

IoT-deNAT: Outbound flow-based network traffic data of IoT and non-IoT devices behind a home NAT

This dataset is comprised of NetFlow records, which capture the outbound network traffic of 8 commercial IoT devices and 5 non-IoT devices, collected during a period of 37 days in a lab at Ben-Gurion University of The Negev. The dataset was collected in order to develop a method for telecommunication providers to detect vulnerable IoT models behind home NATs. Each NetFlow record is labeled with the device model which produced it; for research reproducibilty, each NetFlow is also allocated to either the "training" or "test" set, in accordance with the partitioning described in: Y. Meidan, V. Sachidananda, H. Peng, R. Sagron, Y. Elovici, and A. Shabtai, A novel approach for detecting vulnerable IoT devices connected behind a home NAT, Computers & Security, Volume 97, 2020, 101968, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2020.101968. (http://www.sciencedirect.com/science/article/pii/S0167404820302418)   Please note: <ul> <li>The dataset itself is free to use, however users are requested to cite the above-mentioned paper, which describes in detail the research objectives as well as the data collection, preparation and analysis.</li> <li>Following is a brief description of the features used in this dataset.</li> </ul>   # NetFlow features, used in the related paper for analysis 'FIRST_SWITCHED': System uptime at which the first packet of this flow was switched 'IN_BYTES': Incoming counter for the number of bytes associated with an IP Flow 'IN_PKTS': Incoming counter for the number of packets associated with an IP Flow 'IPV4_DST_ADDR': IPv4 destination address 'L4_DST_PORT': TCP/UDP destination port number 'L4_SRC_PORT': TCP/UDP source port number 'LAST_SWITCHED': System uptime at which the last packet of this flow was switched 'PROTOCOL': IP protocol byte (6: TCP, 17: UDP) 'SRC_TOS': Type of Service byte setting when there is an incoming interface 'TCP_FLAGS': Cumulative of all the TCP flags seen for this flow   # Features added by the authors 'IP': Prefix of the destination IP address, representing the network (without the host) 'DURATION': Time (seconds) between first/last packet switching   # Label 'device_model': <type>.<manufacturer>.<model number>   # Partition 'partition': Training or test   # Additional NetFlow features (mostly zero-variance) 'SRC_AS': Source BGP autonomous system number 'DST_AS': Destination BGP autonomous system number 'INPUT_SNMP': Input interface index 'OUTPUT_SNMP': Output interface index 'IPV4_SRC_ADDR': IPv4 source address 'MAC': MAC address of the source   # Additional data 'category': IoT or non-IoT 'type': IoT, access_point, smartphone, laptop 'date': Datepart of FIRST_SWITCHED 'inter_arrival_time': Time (seconds) between successive flows of the same device (identified by its MAC address)

restrictedJun 2020View details →

zenodo24/100

High-resolution traffic flow data in Glasgow

Description of the dataset This dataset offers a long-term traffic flow data at an intra-city scale with high spatio-temporal granularity. The dataset covers the Glasgow City Council area for four consecutive years spanning the COVID-19 pandemic, from October 2019 to September 2023, providing comprehensive temporal and spatial coverage.  The code used to produce the indicators is available at: <a href="https://github.com/YueLi-0816/TrafficFlowData">https://github.com/YueLi-0816/TrafficFlowData</a>. This work is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China, the ESRC’s ongoing support for the Urban Big Data Centre (UBDC), and the Royal Society International Exchange Scheme. Contents Sensor status metadata - status.csv <table> <tbody> <tr> <td> Column name </td> <td>Description</td> </tr> <tr> <td> id </td> <td>Unique ID for each sensor, e.g., GA0601_T.</td> </tr> <tr> <td> latitude </td> <td>The Latitude in decimal degrees of WGS84 coordinates, e.g., 55.86238129.</td> </tr> <tr> <td> longitude </td> <td>The Longitude in decimal degrees of WGS84 coordinates, e.g., -4.26570708.</td> </tr> <tr> <td> step 1 </td> <td>The status of sensors at the current step is 1 if retained and 0 if removed.</td> </tr> <tr> <td> step 2 </td> <td>The status of sensors at the current step is 1 if retained and 0 if removed.</td> </tr> <tr> <td> step 3 </td> <td>The status of sensors at the current step is 1 if retained and 0 if removed.</td> </tr> <tr> <td> step 4 </td> <td>The status of sensors at the current step is 1 if retained and 0 if removed.</td> </tr> <tr> <td> step 5.1 </td> <td>The status of sensors at the current step is 1 if retained and 0 if removed.</td> </tr> <tr> <td> step 5.2 </td> <td>The status of sensors at the current step is 1 if retained and 0 if removed.</td> </tr> </tbody> </table> Sensor location metadata - locations.csv <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>id</td> <td>Unique ID for each sensor, e.g., GA0601_T.</td> </tr> <tr> <td>latitude</td> <td>The Latitude in decimal degrees of WGS84 coordinates, e.g., 55.86238129.</td> </tr> <tr> <td>longitude</td> <td>The Longitude in decimal degrees of WGS84 coordinates, e.g., -4.26570708.</td> </tr> </tbody> </table>   Traffic flows metadata <table> <tbody> <tr> <td>File name</td> <td>Description</td> </tr> <tr> <td>[sensor_id].csv</td> <td>Traffic flows. [sensor_id] refers to the ‘id’ from the locations.csv</td> </tr> </tbody> </table>   <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>date</td> <td>The date the data is collected (YYYY-MM-DD), e.g., 2021-11-04.</td> </tr> <tr> <td>time</td> <td>The hours of the day the data is collected range from 0 to 23, 0 = [0,1), 23 = [23,24).</td> </tr> <tr> <td>flow</td> <td>Number of vehicles that pass the sensor location during the one-hour interval. </td> </tr> </tbody> </table>

openogl-uk-3.0Jun 2024View details →

dryad24/100

Data from: Rapid transporter regulation prevents substrate flow traffic jams in boron transport

Open the record for dataset details and reuse information.

publicAug 2018View details →

openaire4/100

doi_dedup___::97c0f6fcb2e0e4d6cb87ef23ab4c41d5

Open the record for dataset details and reuse information.

Oct 2010View details →

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