SMRT27 - Individuals who used any internet connected devices or systems for private purposes and problems encountered. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Individuals who used any internet connected devices or systems for private purposes and problems encountered...

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This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"

With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.

For more detail about this dataset, please refer to the paper listed above.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Australia & Oceania and Asia.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES COMPUTERS AND INTERNET USE - DP02 Universe - Total households Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The 2008 Broadband Improvement Act mandated the collection of data about computer and internet use. As a result, three questions were added to the 2013 American Community Survey (ACS) to measure these topics. The computer use question asked if anyone in the household owned or used a computer and included four response categories for a desktop or laptop, a smartphone, a tablet or other portable wireless computer, and some other type of computer. Respondents selected a checkbox for “Yes” or “No” for each response category. Respondents could select all categories that applied. Question asked if any member of the household has access to the internet. “Access” refers to whether or not someone in the household uses or can connect to the internet, regardless of whether or not they pay for the service. If a respondent answers “Yes, by paying a cell phone company or Internet service provider”, they are asked to select the type of internet service.

as they are connected on a large scale with high-value data content

Individuals who used any internet connected devices or... Details Download XLSX Individuals who used any internet connected devices or...

SMRT28 - Individuals who used any internet connected devices or systems for private purposes and problems encountered. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Individuals who used any internet connected devices or systems for private purposes and problems encountered...

Percentage of Internet users by selected Internet service and technology, such as; home Internet access, use of smart home devices, use of smartphones, use of social networking accounts, use or purchase of streaming services, use of government services online and online shopping.

This dataset comprises diverse logs from various sources, including cloud services, routers, switches, virtualization, network security appliances, authentication systems, DNS, operating systems, packet captures, proxy servers, servers, syslog data, and network data. The logs encompass a wide range of information such as traffic details, user activities, authentication events, DNS queries, network flows, security actions, and system events. By analyzing these logs collectively, users can gain insights into network patterns, anomalies, user authentication, cloud service usage, DNS traffic, network flows, security incidents, and system activities. The dataset is invaluable for network monitoring, performance analysis, anomaly detection, security investigations, and correlating events across the entire network infrastructure.

Article Information

The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.

Please do cite the aforementioned article when using this dataset.

Abstract

The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.

ZIP Folder Content

The ZIP folder comprises two main components: Captures and Datasets. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: BLE, IP-Based Packet, and IP-Based Flows.

To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.

This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.

Datasets' Content

Within this dataset, three sub-datasets are available, namely BLE, IP-Based Packet, and IP-Based Flows. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.

Identified Key Features Within Bluetooth Dataset

Feature	Meaning
btle.advertising_header	BLE Advertising Packet Header
btle.advertising_header.ch_sel	BLE Advertising Channel Selection Algorithm
btle.advertising_header.length	BLE Advertising Length
btle.advertising_header.pdu_type	BLE Advertising PDU Type
btle.advertising_header.randomized_rx	BLE Advertising Rx Address
btle.advertising_header.randomized_tx	BLE Advertising Tx Address
btle.advertising_header.rfu.1	Reserved For Future 1
btle.advertising_header.rfu.2	Reserved For Future 2
btle.advertising_header.rfu.3	Reserved For Future 3
btle.advertising_header.rfu.4	Reserved For Future 4
btle.control.instant	Instant Value Within a BLE Control Packet
btle.crc.incorrect	Incorrect CRC
btle.extended_advertising	Advertiser Data Information
btle.extended_advertising.did	Advertiser Data Identifier
btle.extended_advertising.sid	Advertiser Set Identifier
btle.length	BLE Length
frame.cap_len	Frame Length Stored Into the Capture File
frame.interface_id	Interface ID
frame.len	Frame Length Wire
nordic_ble.board_id	Board ID
nordic_ble.channel	Channel Index
nordic_ble.crcok	Indicates if CRC is Correct
nordic_ble.flags	Flags
nordic_ble.packet_counter	Packet Counter
nordic_ble.packet_time	Packet time (start to end)
nordic_ble.phy	PHY
nordic_ble.protover	Protocol Version

Identified Key Features Within IP-Based Packets Dataset

Feature	Meaning
http.content_length	Length of content in an HTTP response
http.request	HTTP request being made
http.response.code	Sequential number of an HTTP response
http.response_number	Sequential number of an HTTP response
http.time	Time taken for an HTTP transaction
tcp.analysis.initial_rtt	Initial round-trip time for TCP connection
tcp.connection.fin	TCP connection termination with a FIN flag
tcp.connection.syn	TCP connection initiation with SYN flag
tcp.connection.synack	TCP connection establishment with SYN-ACK flags
tcp.flags.cwr	Congestion Window Reduced flag in TCP
tcp.flags.ecn	Explicit Congestion Notification flag in TCP
tcp.flags.fin	FIN flag in TCP
tcp.flags.ns	Nonce Sum flag in TCP
tcp.flags.res	Reserved flags in TCP
tcp.flags.syn	SYN flag in TCP
tcp.flags.urg	Urgent flag in TCP
tcp.urgent_pointer	Pointer to urgent data in TCP
ip.frag_offset	Fragment offset in IP packets
eth.dst.ig	Ethernet destination is in the internal network group
eth.src.ig	Ethernet source is in the internal network group
eth.src.lg	Ethernet source is in the local network group
eth.src_not_group	Ethernet source is not in any network group
arp.isannouncement	Indicates if an ARP message is an announcement

Identified Key Features Within IP-Based Flows Dataset

Feature	Meaning
proto	Transport layer protocol of the connection
service	Identification of an application protocol
orig_bytes	Originator payload bytes
resp_bytes	Responder payload bytes
history	Connection state history
orig_pkts	Originator sent packets
resp_pkts	Responder sent packets
flow_duration	Length of the flow in seconds
fwd_pkts_tot	Forward packets total
bwd_pkts_tot	Backward packets total
fwd_data_pkts_tot	Forward data packets total
bwd_data_pkts_tot	Backward data packets total
fwd_pkts_per_sec	Forward packets per second
bwd_pkts_per_sec	Backward packets per second
flow_pkts_per_sec	Flow packets per second
fwd_header_size	Forward header bytes
bwd_header_size	Backward header bytes
fwd_pkts_payload	Forward payload bytes
bwd_pkts_payload	Backward payload bytes
flow_pkts_payload	Flow payload bytes
fwd_iat	Forward inter-arrival time
bwd_iat	Backward inter-arrival time
flow_iat	Flow inter-arrival time
active	Flow active duration

SMRT06 - Individuals who used / did not use any internet-connected devices or systems for private purposes by their reason for not using. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Individuals who used / did not use any internet-connected devices or systems for private purposes by their reason for not using...

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.

Individuals who used any internet connected devices or systems for private purposes and problems encountered

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 CICIoT2023 dataset is a large-scale, realistic intrusion detection dataset designed to support security analytics and machine learning research in the Internet of Things (IoT) domain. Created by the Canadian Institute for Cybersecurity (CIC), the dataset captures 33 different types of attacks (including DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, and Mirai) executed by malicious IoT devices against other IoT targets.

The testbed consists of 105 real IoT devices of different types and manufacturers, including smart home devices and industrial equipment, configured in a complex network topology to emulate real-world conditions. The dataset includes benign and malicious traffic in various formats and supports feature extraction for both traditional ML and deep learning models.

This dataset aims to address the lack of diversity and scale in previous IoT security datasets, offering a robust benchmark for evaluating intrusion detection systems (IDS) and enabling research in IoT cybersecurity, anomaly detection, and network forensics.

Source https://www.mdpi.com/1424-8220/23/13/5941

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.

Smart homes contain programmable electronic devices (mostly IoT) that enable home au- tomation. People who live in smart homes benefit from interconnected devices by controlling them either remotely or manually/autonomously. However, high interconnectivity comes with an increased attack surface, making the smart home an attractive target for adversaries. NCC Group and the Global Cyber Alliance recorded over 12,000 attacks to log into smart home devices maliciously. Recent statistics show that over 200 million smart homes can be subjected to these attacks. Conventional security systems are either focused on network traffic (e.g., firewalls) or physical environment (e.g., CCTV or basic motion sensors), but not both. A key challenge in de- veloping cyber-physical security systems is the lack of datasets and test beds. For cyber-physical datasets to be meaningful, they need to be collected in real smart home environments. Due to the inherited difficulties and challenges (e.g. effort, costs, test-bed availability), such cyber-physical smart home datasets are quite rare. This paper aims to fill this gap by contributing a dataset we collected in a real smart home with annotated labels. This paper explains the process we followed to collect the data and how we organised them to facilitate wider use within research communities.A related article can be found at https://doi.org/10.3389/friot.2023.1275080

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This dataset contains information on internet service subscribers, including their gender, area of residence, internet service provider (ISP) name, connection type, router details, number of devices, average downtime in minutes, cost in Bangladeshi Taka (BDT), internet speed in megabits per second (Mbps), duration of service with the current ISP, rating of technical support quality, recommendation likelihood, and agreement to use provided information for research purposes. The data provides insights into the demographics and service experiences of internet users in Bangladesh, encompassing various locations, ISPs, connection types, and user satisfaction levels.