In the first half of 2021, video accounted for over **** of global traffic. Social occupied the next largest share at **** percent, while web browsing accounted for around a *****. Audio accounted for only **** percent of traffic worldwide.

This statistic gives information on the consumer internet data traffic worldwide from 2016 to 2022, by application category. In 2017, the global consumer data traffic from internet video amounted to ** exabytes per month.

In January 2025 mobile devices excluding tablets accounted for over ** percent of web page views worldwide. Meanwhile, over ** percent of webpage views in Africa were generated via mobile. In contrast, just over half of web traffic in North America still took place via desktop connections with mobile only accounting for **** percent of total web traffic. While regional infrastructure remains an important factor in broadband vs. mobile coverage, most of the world has had their eyes on the recent 5G rollout across the globe, spearheaded by tech-leaders China and the United States. The number of mobile 5G subscriptions worldwide is forecast to reach more than ***** billion by 2028. Social media: room for growth in Africa and southern Asia Overall, more than ** percent of the world’s mobile internet subscribers are also active on social media. A fast-growing market, with newcomers such as TikTok taking the world by storm, marketers have been cashing in on social media’s reach. Overall, social media penetration is highest in Europe and America while in Africa and southern Asia, there is still room for growth. As of 2021, Facebook and Google-owned YouTube are the most popular social media platforms worldwide. Facebook and Instagram are most effective With nearly ***** billion users, it is no wonder that Facebook remains the social media avenue of choice for the majority of marketers across the world. Instagram, meanwhile, was the second most popular outlet. Both platforms are low-cost and support short-form content, known for its universal consumer appeal and answering to the most important benefits of using these kind of platforms for business and advertising purposes.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.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 number of internet users in countries like the Americas and Asia.

Streaming is by far the predominant type of traffic in communication networks. With thispublic dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3G/4G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

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.

When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Mobile Data Traffic Market Outlook

The global mobile data traffic market size was estimated at approximately USD 68 billion in 2023 and is projected to surge to about USD 320 billion by 2032, exhibiting a remarkable compound annual growth rate (CAGR) of 18.5% over the forecast period. This growth is driven by the increasing penetration of smartphones, advancements in network technologies, and the rising consumption of data-intensive applications and services.

One of the primary growth factors for the mobile data traffic market is the rapid expansion of the smartphone user base globally. As smartphones become more affordable and accessible, especially in emerging markets, the number of mobile internet users is skyrocketing. This trend is further amplified by the increasing availability of high-speed mobile networks, which make data-heavy applications such as video streaming and online gaming more feasible and attractive to users. The proliferation of affordable data plans is also encouraging users to consume more mobile data, thereby bolstering market growth.

Another significant driver of growth is the continuous evolution of network technologies. The transition from 3G to 4G, and now to 5G, has significantly enhanced data transmission speeds and network capabilities. 5G technology, in particular, promises ultra-low latency, higher capacity, and faster download and upload speeds, which are expected to revolutionize various sectors such as healthcare, automotive, and smart cities. The deployment and adoption of 5G networks are anticipated to boost mobile data traffic volumes exponentially, as it facilitates the seamless use of high-bandwidth applications, including augmented reality (AR), virtual reality (VR), and Internet of Things (IoT) devices.

The increase in video content consumption is also a major factor driving the market. Video traffic accounts for a substantial portion of mobile data usage, driven by platforms like YouTube, Netflix, and social media sites that prioritize video content. The trend of live streaming and video-on-demand services is creating a massive surge in data traffic, with users increasingly accessing high-definition (HD) and even 4K content. Moreover, the COVID-19 pandemic has accelerated the adoption of digital entertainment and online education, further increasing the demand for mobile data.

Regionally, the growth of mobile data traffic is witnessing variations with Asia Pacific leading the charge. The region's high population density, coupled with increasing urbanization and smartphone penetration, makes it a significant contributor to global data traffic. Countries like China and India are at the forefront, driven by government initiatives to promote digitalization and the rollout of advanced mobile networks. North America and Europe are also substantial markets due to their well-established network infrastructure and early adoption of new technologies. However, the growth rates in these regions are relatively moderate compared to the exponential growth seen in Asia Pacific and Latin America.

Traffic Type Analysis

The mobile data traffic market can be segmented by traffic type into video, audio, data, and others. Video traffic is the most dominant segment, accounting for the largest share of mobile data usage worldwide. The proliferation of video streaming services, alongside user-generated video content on social media platforms, significantly contributes to this dominance. As more users switch to high-definition and 4K streaming, the demand for data-intensive video content continues to rise. Additionally, the growing popularity of live streaming and video calls, particularly in the context of remote work and online education, further propels this segment's growth.

Audio traffic also plays a significant role in the mobile data traffic market. The increasing usage of music streaming services such as Spotify, Apple Music, and various podcast platforms are driving the growth of this segment. The trend of consuming audio content on the go, facilitated by improved network speeds and unlimited data plans, is contributing to a steady rise in mobile data traffic from audio services. Furthermore, the adoption of smart speakers and voice assistant technologies is expected to continue bolstering this segment.

Data traffic, encompassing all forms of non-visual and non-audio data, is another crucial segment. This includes browsing, app usage, emails, and other types of data transmission over mobile networks. With the increasing reliance on mobile applications for a wide array of activities—ra

This resource includes input data used in the work "Machine-Learning Based Prediction of Multiple Types of Network Traffic" by Aleksandra Knapińska, Piotr Lechowicz, and Krzysztof Walkowiak; published in International Conference on Computational Science (ICCS) 2021, Lecture Notes in Computer Science, vol 12742. pp. 122-136. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_12 The work was supported by the National Science Centre, Poland, under Grant 2019/35/B/ST7/04272. Both seattle_november.xml and seattle_december.xml files include internet traffic data from Seattle Internet Exchange Point. The european.xml file includes internet traffic data from one of the European Internet Exchange Points. Each file includes the traffic volume decomposed into specific frame size ranges. Each file starts with a metadata section providing general information. The period covered by a specific file is indicated by its 'start' and 'end' tags. They provide Unix timestamps in the GMT timezone. It should be noted that Seattle lies in the PST time zone, and the European IXP is located in the CET timezone, so the start and end times should be adjusted accordingly. The step parameter is given in seconds, so the samples are stored every 5 minutes in all three files. Each file has multiple columns providing traffic data in bits per second for different frame size ranges. Column names specify the ranges in bytes. The 'total' column stores information about the total aggregate traffic volume, which is a sum of values in all the remaining columns in each row.

When asked about "Attitudes towards the internet", most Mexican respondents pick "It is important to me to have mobile internet access in any place" as an answer. 56 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Context This dataset is a consolidated and cleaned CSV version of the ISCX VPN-nonVPN 2016 dataset from the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick. The original dataset was created to characterize and identify different types of network traffic, which is crucial for network management, Quality of Service (QoS) optimization, and cybersecurity.

This single CSV file combines the multiple .arff files from the original dataset, making it easier to use for data analysis and machine learning projects in Python.

Content The dataset contains network flow features extracted from packet captures (PCAPs). Each row represents a single network flow and has been labeled with the specific application type and whether it was routed through a VPN.

Features (X): Include over 20 time-related flow features like duration, flowBytesPerSecond, flowPktsPerSecond, min_active, max_idle, etc. These features describe the timing, duration, and volume of the data flows.

Target (y): The target column, traffic_type, is a multi-class label describing the application and connection type (e.g., VPN-CHAT, NonVPN-STREAMING, VPN-Browse).

Potential Uses & Inspiration 🚀 Multi-Class Classification: Can you build a model to accurately identify the specific application generating the traffic?

Binary Classification: Can you distinguish between VPN and Non-VPN traffic, regardless of the application?

Resource Allocation: Predict which types of traffic (e.g., Streaming) require more bandwidth, helping to build smarter network management tools.

Federated Learning: This dataset is ideal for simulating a Federated Learning environment where data from different "users" (applications) is used to train a central model without sharing raw data.

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }

The global traffic control cabinet market, valued at $2.869 billion in 2025, is projected to experience steady growth, driven by increasing urbanization, rising investments in smart city infrastructure, and the growing need for efficient traffic management systems worldwide. The market's 3.8% CAGR indicates a consistent expansion through 2033, fueled by advancements in adaptive control cabinet technology offering improved traffic flow optimization and reduced congestion. Key application areas, such as urban transportation and public facilities, are experiencing significant growth, as cities worldwide prioritize improving road safety and traffic efficiency. The market is segmented by type, with timing control cabinets and adaptive control cabinets dominating the market share. Adaptive control cabinets are gaining traction due to their ability to dynamically adjust traffic signals based on real-time traffic conditions, thus optimizing traffic flow and reducing travel times. Major players in the market include SWARCO, Bison Profab, and others, competing on factors like technological innovation, product features, and geographical reach. While challenges remain, such as high initial investment costs for advanced systems and potential cybersecurity vulnerabilities, the long-term growth prospects remain positive due to ongoing government initiatives promoting smart city development and sustainable transportation solutions. The regional distribution of the market reflects global urbanization patterns, with North America and Europe holding substantial market shares due to advanced infrastructure and technological adoption. However, Asia-Pacific is expected to witness significant growth in the coming years driven by rapid urbanization and infrastructure development in countries like China and India. The competitive landscape is characterized by both established international players and regional manufacturers. The market is likely to see further consolidation through mergers and acquisitions as companies strive to expand their product portfolios and global reach. Technological advancements, such as the integration of artificial intelligence and the Internet of Things (IoT) in traffic management systems, are expected to drive innovation and shape the future of the traffic control cabinet market. The focus on sustainable and energy-efficient solutions is also a significant factor shaping the market's trajectory.

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)

When asked about "Attitudes towards the internet", most Chinese respondents pick "It is important to me to have mobile internet access in any place" as an answer. 50 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

This resource includes input data used in the work "Long-term prediction of multiple types of time-varying network traffic using chunk-based ensemble learning" by Aleksandra Knapińska, Piotr Lechowicz, Weronika Węgier, and Krzysztof Walkowiak. The work was supported by the National Science Centre, Poland, under Grants 2019/35/B/ST7/04272, 2018/31/D/ST6/0304, and 2019/35/B/ST6/04442.
The SIX2021.xml file includes internet traffic data from the Seattle Internet Exchange Point collected for one year. The file contains information about the traffic volume decomposed into specific frame size ranges. It starts with a metadata section providing general information. The covered period is indicated by the 'start' and 'end' tags. They provide Unix timestamps in the GMT timezone. It should be noted that Seattle lies in the PST time zone, so the start and end times should be adjusted accordingly. The step parameter is given in seconds, so the samples are stored every 5 minutes. The file has multiple columns providing traffic data in bits per second for different frame size ranges. Column names specify the ranges in bytes. The 'total' column stores information about the total aggregate traffic volume, which is a sum of values in all the remaining columns in each row.

This dataset presents network traffic traces data of the 14 D-Link IoT devices from different types including camera, network camera, smart-plug, door-window sensor, and home-hub. It consists of:

• Network packet traces (inbound and outbound traffic) and
• IEEE 802.11 MAC frame traces.

The experimental testbed was set-up in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD) to collect all the network traffic traces from 9th September 2020 to 10th January 2021 including an access point on a laptop. The network traffic traces were captured passively observing the Ethernet interface and the WiFi interface at the access point.

In packet traces, typical communication protocols, such as TCP, UDP, IP, ICMP, ARP, DNS, SSDP, TLS/SSL etc, data are captured which IoT devices use for communication on the Internet. In the probe request frame (a subtype of management frames) traces, data are recorded which IoT devices use to connect access point on the local area network.

The authors would like to thank the Faculty of Integrated Technologies, Universiti Brunei Darussalam, for the support to conduct this research experiment in the Network Systems and Signal Processing laboratory.

The average daily internet traffic per capita in Romania has been increasing over the observed period, for both broadband and mobile internet connections. As a result, the average daily mobile internet traffic per person was around *** megabytes in the first half of 2024.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) The aircraft landing dataset contains data about aircraft landings at SFO with monthly landing counts and landed weight by airline, region and aircraft model and type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. D. HOW TO USE THIS DATASET Airport data is seasonal in nature; therefore, any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics