Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

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.

This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

Construction site coordination in Hamburg The preservation of the infrastructure is of fundamental importance for the development of Hamburg. Therefore, construction sites in the street space are part of the normal picture - to the chagrin of local residents and road users. In many cases, however, it is not work on the road itself that leads to disabilities, but the many supply and disposal lines in the road body or the construction projects of private individuals. Approximately 25,000 jobs per year on Hamburg's road network, of which over 3,700 are on major roads, therefore require careful coordination to minimise obstacles to traffic flow. This is the task of the Traffic Optimization Department at the Department of Transport and Mobility Transition. Here, the incoming information of all road construction departments, pipeline companies and private builders is collected and evaluated. The information for the most important construction sites is published with a 7-day preview on the Internet at www.hamburg.de/baustellen. When coordinating construction sites, the aim is to prevent simultaneous construction sites, e.g. on important parallel roads, so that traffic has trouble-free alternative routes. However, no matter how good coordination can absolutely prevent congestion. The Hamburg road network is partly busy and partly overloaded in the morning and evening rush hour. Therefore, we recommend every road user to inform himself about the current traffic situation before starting the journey and only then to choose a suitable means of transport including route.

If you have any questions about construction sites in Hamburg, please contact the construction site hotline on 040 428 28 2020 or by post to

Free and Hanseatic City of Hamburg Transport and Mobility Transition Authority Old stone path 4 20459 Hamburg

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.The color-coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation, and field operations. A color-coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes.The map also includes dynamic traffic incidents showing the location of accidents, construction, closures, and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis.Data sourceEsri’s typical speed records and live and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.Data coverageThe service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

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:

• 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.
• Following is a brief description of the features used in this dataset.

# 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':

# 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)

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly). Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

This is the official website for downloading the CA sub-dataset of the LargeST benchmark dataset. There are a total of 7 files in this page. Among them, 5 files in .h5 format contain the traffic flow raw data from 2017 to 2021, 1 file in .csv format provides the metadata for sensors, and 1 file in .npy format represents the adjacency matrix constructed based on road network distances. Please refer to https://github.com/liuxu77/LargeST for more information.

A dataset providing information of the vehicle types and counts in several locations in Leeds. Purpose of the project The aim of this work was to examine the profile of vehicle types in Leeds, in order to compare local emissions with national predictions. Traffic was monitored for a period of one week at two Inner Ring Road locations in April 2016 and at seven sites around the city in June 2016. The vehicle registration data was then sent to the Department for Transport (Dft), who combined it with their vehicle type data, replacing the registration number with an anonymised ‘Unique ID’. The data is provided in three folders:- Raw Data – contains the data in the format it was received, and a sample of each format. Processed Data – the data after processing by LCC, lookup tables, and sample data. Outputs – Excel spreadsheets summarising the data for each site, for various time/dates. Initially a dataset was received for the Inner Ring Road (see file “IRR ANPR matched to DFT vehicle type list.csv”), with vehicle details, but with missing / uncertain data on the vehicles emissions Eurostandard class. Of the 820,809 recorded journeys, from the pseudo registration number field (UniqueID) it was determined that there were 229,891 unique vehicles, and 31,912 unique “vehicle types” based on the unique concatenated vehicle description fields. It was therefore decided to import the data into an MS Access database, create a table of vehicle types, and to add the necessary fields/data so that combined with the year of manufacture / vehicle registration, the appropriate Eurostandard could be determined for the particular vehicle. The criteria for the Eurostandards was derived mainly from www.dieselnet.com and summarised in a spreadsheet (“EuroStandards.xlsx”). Vehicle types were assigned to a “VehicleClass” (see “Lookup Tables.xlsx”) and “EU class” with additional fields being added for any modified data (Gross Vehicle Weight – “GVM_Mod”; Engine capacity – “EngineCC_mod”; No of passenger seats – “PassSeats”; and Kerb weight – “KerbWt”). Missing data was added from the internet lookups, extrapolation from known data, and by association – eg 99% of cars with an engine size Additional data was then received from the Inner Ring Road site, giving journey date/time and incorporating the Taxi data for licensed taxis in Leeds. Similar data for Sites 1-7 was also then received, and processed to determine the “VehicleClass” and “EU class”. A mixture of Update queries, and VBA processing was then used to provide the Level 1-6 breakdown of vehicle types (see “Lookup Tables.xlsx”). The data was then combined into one database, so that the required Excel spreadsheets could be exported for the required time/date periods (see “outputs” folder).

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Vehicle travel time and delay data on sections of road in Hamilton City, based on Bluetooth sensor records. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_link_stats?Page=1&Start_Date=2021-06-02&End_Date=2021-06-03. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

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Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

INFO ABOUT THE BOT-IOT DATASET, NOTE: only the csv files stated in the description are used

The BoT-IoT dataset can be downloaded from HERE. You can also use our new datasets: the TON_IoT and UNSW-NB15.

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The BoT-IoT dataset was created by designing a realistic network environment in the Cyber Range Lab of UNSW Canberra. The network environment incorporated a combination of normal and botnet traffic. The dataset’s source files are provided in different formats, including the original pcap files, the generated argus files and csv files. The files were separated, based on attack category and subcategory, to better assist in labeling process.

The captured pcap files are 69.3 GB in size, with more than 72.000.000 records. The extracted flow traffic, in csv format is 16.7 GB in size. The dataset includes DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used.

To ease the handling of the dataset, we extracted 5% of the original dataset via the use of select MySQL queries. The extracted 5%, is comprised of 4 files of approximately 1.07 GB total size, and about 3 million records.

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Free use of the Bot-IoT dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes should be agreed by the authors. The authors have asserted their rights under the Copyright. To whom intent the use of the Bot-IoT dataset, the authors have to cite the following papers that has the dataset’s details: .

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Benjamin Turnbull. "Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset." Future Generation Computer Systems 100 (2019): 779-796. Public Access Here.

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay. "Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques." In International Conference on Mobile Networks and Management, pp. 30-44. Springer, Cham, 2017.

Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova. "A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework." Future Generation Computer Systems 110 (2020): 91-106.

Koroniotis, Nickolaos, and Nour Moustafa. "Enhancing network forensics with particle swarm and deep learning: The particle deep framework." arXiv preprint arXiv:2005.00722 (2020).

Koroniotis, Nickolaos, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, and Helge Janicke. "A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports." IEEE Access (2020).

Koroniotis, Nickolaos. "Designing an effective network forensic framework for the investigation of botnets in the Internet of Things." PhD diss., The University of New South Wales Australia, 2020.

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Switzerland is leading the ranking by population share with mobile internet access, recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

IL Coverage of the Gateway camera snapshots. The Gateway provides camera snapshot images throughout its coverage area in the form of camera icons on its maps and images in its camera report. With a free subscription, users can also access the Gateway ftp server which contains the most up to date versions of the images available.ImgPath - this is a link to the travelmidwest.com/lmiga/showCamera.jsp popup window that allows the user to select another direction, if availableCameraLocation - a text description of where the camera is locatedCameraDirection - the direction the camera is facing (NONE, N, E, S, W, NE, NW, SE, or SW)y - latitude in decimal degreesx - longitude in decimal degreesSnapShot - public URL of camera's image file that is suitable for placement in a tag, for instanceWarningAge - "true" if the camera is more than 10 minutes old, false otherwiseTooOld - "true" if more than 30 minutes old, "false" otherwiseAgeInMinutes - integer age of camera image in minutes