The dataset contains all data used during the evaluation of statistical characteristics preservation. Archives are protected by password "trace-share" to avoid false detection by antivirus software.

For more information, see the project repository at https://github.com/Trace-Share.

Selected Attack Traces

We selected 72 different traces of network attacks obtained from various internet databases. File names refer to common names of contained vulnerabilities, malware, or attack tools.

Background Traffic Data

Publicly available dataset CSE-CIC-IDS-2018 was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.

Evaluation Results and Dataset Structure

• Traces variants (traces-normalized.zip, traces-adjusted.zip)
  • ./traces-normalized/ — normalized PCAP files and details in YAML format;
  • ./traces-adjusted/ — configuration files for traces combination in YAML format.
• Computed statistics (statistics.zip)
  • ./statistics-background/ — background traffic statistics computed by ID2T;
  • ./statistics-combination/ — combined traces statistics computed by ID2T for all adjust options (selected only combinations where ID2T provided all statistics files);
  • ./statistics-difference/ — computed mean and median differences of background and combined traffic traces.
• Evaluation results
  • statistics-difference.ipynb — file containing visualization of statistics differences.

Context There's a story behind every dataset and here's your opportunity to share yours.

Content What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration Your data will be in front of the world's largest data science community. What questions do you want to see answered?

European Conventional Passenger Trains Traffic Share by Country (Thousand Train-Kilometers), 2023 Discover more data with ReportLinker!

Barrier: Captures jersey barriers and walls along roads and bridges as linear features. This includes impact attenuators. Where road edges and barriers share the same delineation, the geometry is coincident.Guardrail: Captures guardrails along roads and bridges as linear features. This includes impact attenuators. Where road edges and guardrails share the same delineation, the geometry is coincident.Hidden Barrier: Captures hidden jersey barriers and walls along roads and bridges as linear features. Hidden barriers are features that run under other features such as bridges, overpasses, or tunnels and are not fully visible in an aerial photograph. The location is interpreted based on the information visible on either side of the visible feature. Where road edges and barriers share the same delineation, the geometry is coincident.Hidden Guardrail: Captures hidden guardrails and walls along roads and bridges as linear features. Hidden guardrails are features that run under other features such as bridges, overpasses, or tunnels and are not fully visible in an aerial photograph. The location is interpreted based on the information visible on either side of the visible feature. Where road edges and guardrails share the same delineation, the geometry is coincident.

The dataset contains all data used during the evaluation of trace meaning preservation. Archives are protected by password "trace-share" to avoid false detection by antivirus software.

For more information, see the project repository at https://github.com/Trace-Share.

Selected Attack Traces

The following list contains trace datasets used for evaluation. Each attack was chosen to have not only a different meaning but also different statistical properties.

• dos_http_flood — the capture of GET and POST requests sent to one server by one attacker (HTTP~traffic);
• ftp_bruteforce — short and unsuccessful attempt to guess a user’s password for FTP service (FTP traffic);
• ponyloader_botnet — Pony Loader botnet used for stealing of credentials from 3 target devices reporting to single IP with a large number of intermediate addresses (DNS and HTTP traffic);
• scan — the capture of nmap tool that scans given subnet using ICMP echo and TCP SYN requests (consist of ARP, ICMP, and TCP traffic);
• wannacry_ransomware — the capture of Wanacry ransomware that spreads in a domain with three workstations, a domain controller, and a file-sharing server (SMB and SMBv2 traffic).

Background Traffic Data

Publicly available dataset CSE-CIC-IDS-2018 was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.

Evaluation Results and Dataset Structure

• Traces variants (traces.zip)
  • ./traces-original/ — trace PCAP files and crawled details in YAML format;
  • ./traces-normalized — normalized PCAP files and details in YAML format;
  • ./traces-adjusted — adjusted PCAP files using various timestamp generation settings, combination configuration in YAML format, and lables provided by ID2T in XML format.
• Extracted alerts (alerts.zip)
  • ./alerts-original/ — extracted Suricata alerts, Suricata log, and full Suricata output for all original trace files;
  • ./alerts-normalized/ — extracted Suricata alerts, Suricata log, and full Suricata output for all normalized trace files;
  • ./alerts-adjusted/ — extracted Suricata alerts, Suricata log, and full Suricata output for all adjusted trace files.
• Evaluation results
  • *.csv files in the root directory — data contains extracted alert signatures and their count per each trace variant.

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

This dataset contains the Market Share in Passenger and Cargo Traffic Carried on Scheduled Domestic Services. It includes cargo carried, scheduled domestic passengers carried, revenue tonne and revenue passenger kilometres performed.

This table contains data on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode, for California, its regions, counties, county divisions, cities/towns, and census tracts. Injury data is from the Statewide Integrated Traffic Records System (SWITRS), California Highway Patrol (CHP), 2002-2010 data from the Transportation Injury Mapping System (TIMS) . The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity]. Transportation accidents are the second leading cause of death in California for people under the age of 45 and account for an average of 4,018 deaths per year (2006-2010). Risks of injury in traffic collisions are greatest for motorcyclists, pedestrians, and bicyclists and lowest for bus and rail passengers. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience 4 times the death rate as Whites or Asians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

NOTE: The Historic Traffic Data Dashboard & Feature Hosted Service have been retired.Network operations traffic data from Main Roads Western Australia for 2015 to 2019. The data provided includes data collected on the Perth Metropolitan State Road Network (PMSRN) at 15 minute intervals. The Historic Traffic Data is provided in CSV format per year. Each table has over 34 million rows and can be linked to the M-Links Road Network using the M-Links ID. A data dictionary for M-Links Road Network and the Historic Traffic Data is at the following link:https://bit.ly/2S86uSnNetwork Operations traffic data can also be accessed via the Daily Traffic Data API at the following link: https://bit.ly/34ZsyAK The network operations traffic data provided here is of variable quality and has not been checked, quality assured or manually corrected. An automated process is used to patch over missing or suspect data with the most representative data available within the database. Patches may be reapplied as new data becomes available and patched data may change over time. Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- “The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.”

Dataset including all data for samples analyzed in Darling et al. 2018 Environmental Science & Technology. Includes OTU counts for all samples, taxonomic assignments for all OTUs, variables associated with vessels, and family-level counts across all vessels (used for indicator analysis). File also includes metadata describing all variables. This dataset is associated with the following publication: Darling, J., J. Martinson, Y. Gong, S. Okum, E. Pilgrim, K. Pagenkopp Lohan, J. Carney, and G. Ruiz. Ballast Water Exchange and Invasion Risk Posed by Intracoastal Vessel Traffic: An Evaluation Using High Throughput Sequencing. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(17): 9926-9936, (2018).

The location of electronic traffic signals, designed, owned and or controlled and maintained by Main Roads Western Australia, that control vehicle and pedestrian traffic at an intersection or on a road are identified in this data set. The location of electronic traffic signals, designed, owned and or controlled and maintained by Main Roads Western Australia, that control vehicle and pedestrian traffic at an intersection or on a road are identified in this data set. The signal can be red, yellow, green or white light displays, and can include circular and arrow signals, pedestrian signals, bicycle crossing signals, B (bus) signals, overhead lane control signals, and twin red or yellow signals. This dataset was developed to identify the location of Main Roads' controlled electronic signals across Western Australia and assist in the management of this asset. Additionally, it records attribute information which includes the LM No (Asset ID.), Service Status, Signal Type, Intersection Name and Intersection Description.

In this dataset, we provide detailed traffic stream data for the Spot robot, including both the Spot robot control traffic stream and the Spot video stream. The Spot robot traffic streams provide realistic traffic data for communication network evaluations, e.g., for measurements with the TSN FlexText testbed. Furthermore, we share data for the tactile internet including audio, video, and robotic communication. Finally, the dataset includes generic data streams for three different intervals (0.2ms, 0.3ms, and 0.5ms) with two different Ethernet frame sizes. The data is provided as .*pcap which can be replayed with various tools or be analyzed, e.g., with Wireshark. The Spot data streams are split into two directions and are based on Spot API calls.

Traffic Analysis Zones for the North Jersey Regional Transportation Model-Enhanced (NJRTM-E).Zonal System: There are 2,712 traffic analysis zones (over 1,600 of these are in the NJTPA region). The model now includes all of New York City and Long Island, portions of southern New Jersey, portions of southern New York State, and portions of eastern Pennsylvania.

The indicator describes the share of the traffic area in the area. High indicator values ​​occur especially a. in territorial units where cross-regional transport hubs are located. Further information at http://www.ioer-monitor.de/index.php?id=44&ID_IND=V01RG. Registration is required to use WCS and WFS services. Please register at https://monitor.ioer.de/monitor_api/signup.

This data shows traffic volumes for freeways (excluding toll roads) and arterial roads in Victoria. The annual average daily traffic volume is provided, including the number of commercial vehicles. The data provided is for the current year, with values derived from traffic surveys or estimates.

Ardgillan Demense Traffic Data 2024 2027 FCC. Published by Fingal County Council. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Data on Traffic Volume entering to Ardgillan Demesne - 2024 onwardsArdgillan park is unique among Dublin’s regional parks for the magnificent views it enjoys of the coastline. A panorama, taking in Rockabill Lighthouse, Colt Church, Shenick and Lambay Islands may be seen, including Sliabh Foy, the highest of the Cooley Mountains, and of course the Mourne Mountains can be seen sweeping down to the sea.The park area is the property of Fingal County Council and was opened to the public as a regional park in June 1985. Preliminary works were carried out prior to the opening in order to transform what had been an arable farm, into a public park. Five miles of footpaths were provided throughout the demesne, some by opening old avenues, while others were newly constructed. They now provide a system of varied and interesting woodland, walks and vantage points from which to enjoy breath-taking views of the sea, the coastline and surrounding countryside. A signposted cycle route through the park since June 2009 means that cyclists can share the miles of walking paths with pedestriansAttractions within the DemesnePlay GroundRose GardensFair TrailPollinator Areas ( Approx. 40 Acres on whole Demesne)CafeCycle Track Walking Routes See further details on web site https://ardgillancastle.ie/...

Portable Traffic Counts Line data to join to table data.

The Harmonised Traffic Counts dataset reports on the number and type of vehicles travelling past traffic counter stations across Australia in a given duration, over time. Depending on the sophistication of the counter and the provider, counts can be by vehicle classification (using the AustRoads 1-12 classification), a 2 or 4-level grouping of the 12-level classification or a simple count of all traffic. The data is sourced from a number of providers, either state authorities or commercial data collection facilitators, who publish open traffic counts. Count observations can be hourly, by calendar year or financial year – this depends on what each jurisdiction releases. The NFDH is piloting the harmonisation and release of this data set, and has combined traffic count information from the following states: NSW, QLD, SA, TAS, and VIC. The extracts linked below are only samples of the full harmonised data set.

The Travel Monitoring Analysis System (TMAS) - Volume dataset was compiled on December 31, 2023 and was published on July 16, 2024 from the Federal Highway Administration (FHWA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TMAS data included in this table have been collected by the FHWA from State DOTs through (temporal data representing each time period) permanent count data. DOTs determine what volume data is reported for any given month or day within the month. Each record in the volume data for the reported site, direction or lane is for the given day of record (it contains all 24 hours of data). The attributes are used by FHWA for its Travel Monitoring Analysis System and external agencies and have been intentionally limited to location referencing attributes since the core station description attribute data are contained within TMAS. The attributes in the Volume data correspond with the Volume file format found in Chapter 6 of the 2001 Traffic Monitoring Guide (https://doi.org/10.21949/1519109).