Android maintained its position as the leading mobile operating system worldwide in the first quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.

Internet Usage: Operating System Market Share: Mobile: Tizen data was reported at 0.000 % in 30 Aug 2024. This stayed constant from the previous number of 0.000 % for 29 Aug 2024. Internet Usage: Operating System Market Share: Mobile: Tizen data is updated daily, averaging 0.000 % from May 2024 (Median) to 30 Aug 2024, with 18 observations. The data reached an all-time high of 0.140 % in 26 Aug 2024 and a record low of 0.000 % in 30 Aug 2024. Internet Usage: Operating System Market Share: Mobile: Tizen data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Mongolia – Table MN.SC.IU: Internet Usage: Operating System Market Share.

San Marino Internet Usage: Operating System Market Share: Mobile: KaiOS data was reported at 0.000 % in 22 Feb 2025. This stayed constant from the previous number of 0.000 % for 21 Feb 2025. San Marino Internet Usage: Operating System Market Share: Mobile: KaiOS data is updated daily, averaging 0.000 % from Oct 2024 (Median) to 22 Feb 2025, with 134 observations. The data reached an all-time high of 0.870 % in 15 Nov 2024 and a record low of 0.000 % in 22 Feb 2025. San Marino Internet Usage: Operating System Market Share: Mobile: KaiOS data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s San Marino – Table SM.SC.IU: Internet Usage: Operating System Market Share.

Uzbekistan Internet Usage: Operating System Market Share: Mobile: Tizen data was reported at 0.000 % in 25 Jan 2025. This records a decrease from the previous number of 0.040 % for 24 Jan 2025. Uzbekistan Internet Usage: Operating System Market Share: Mobile: Tizen data is updated daily, averaging 0.000 % from Jan 2025 (Median) to 25 Jan 2025, with 6 observations. The data reached an all-time high of 0.040 % in 24 Jan 2025 and a record low of 0.000 % in 25 Jan 2025. Uzbekistan Internet Usage: Operating System Market Share: Mobile: Tizen data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Uzbekistan – Table UZ.SC.IU: Internet Usage: Operating System Market Share.

This dataset provides comprehensive information about various Samsung smartphones, including their dimensions, system-on-chip (SoC), central processing unit (CPU), graphics processing unit (GPU), RAM, storage capacity, display specifications, battery details, operating system (OS), and camera attributes. Each row represents a different Samsung smartphone model, and the dataset contains valuable data for comparative analysis, research, or exploring the features of these smartphones. With details on multiple key specifications, this dataset is a valuable resource for tech enthusiasts, consumers, and analysts interested in Samsung's mobile offerings.

The dataset offers a structured format for easily comparing and contrasting different Samsung smartphone models, making it a valuable tool for decision-making, market analysis, and understanding the evolving landscape of Samsung's mobile devices.

The energy consumption of Android devices, measured via data collection from features, is a recurring theme in the literature. To evaluate the performance of such devices, databases are generated by collecting data from features while using the Android operating system. This is a database generated using Tucandeira Data Collector from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 10,331,114 records related to dynamic, background, list of applications, and static data. Device records were collected daily from ten distinct devices and stored in CSV files that were later organized to generate a database by cleaning and preprocessing the data that are publically available in the Mendeley Data Repository. The dataset formed an integral component of the SWPERFI RD&I Project, a research, development, and innovation initiative aimed at improving the performance and energy optimization of mobile devices. This project was undertaken at the Federal University of Amazonas.

Cayman Islands Internet Usage: Operating System Market Share: Mobile: KaiOS data was reported at 0.000 % in 02 Feb 2025. This stayed constant from the previous number of 0.000 % for 01 Feb 2025. Cayman Islands Internet Usage: Operating System Market Share: Mobile: KaiOS data is updated daily, averaging 0.000 % from Jan 2025 (Median) to 02 Feb 2025, with 9 observations. The data reached an all-time high of 0.070 % in 29 Jan 2025 and a record low of 0.000 % in 02 Feb 2025. Cayman Islands Internet Usage: Operating System Market Share: Mobile: KaiOS data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Cayman Islands – Table KY.SC.IU: Internet Usage: Operating System Market Share.

Analysis of ‘Android Phones’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/khaiid/android-phones on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Android is the most used operating systems in the mobile phones field, it would be interesting to explore the different manufacturers and devices that uses it and which versions of Android operating system are widely used

Content

The data has about 1300 rows including 4 attributes described as following:

Name: Mobile phone name Brand: Manufacturer brand name Release: Release date of the mobile Version: Android version of the mobile

Questions to be answered

How many phones use Android 11 ? Which phones were released the latest ? Which brand has the most phones released ? How many brands are there

Data Collection

This Data uses material from ( https://en.wikipedia.org/wiki/List_of_Android_smartphones ) which is released under the Creative Commons Attribution-Share-Alike License 3.0

--- Original source retains full ownership of the source dataset ---

Peru Internet Usage: Operating System Market Share: Mobile: KaiOS data was reported at 0.000 % in 02 Dec 2024. This stayed constant from the previous number of 0.000 % for 01 Dec 2024. Peru Internet Usage: Operating System Market Share: Mobile: KaiOS data is updated daily, averaging 0.000 % from Nov 2024 (Median) to 02 Dec 2024, with 9 observations. The data reached an all-time high of 0.060 % in 28 Nov 2024 and a record low of 0.000 % in 02 Dec 2024. Peru Internet Usage: Operating System Market Share: Mobile: KaiOS data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Peru – Table PE.SC.IU: Internet Usage: Operating System Market Share.

For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:

First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.

Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.

To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.

For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.

The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as web logs and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.

The entire dataset creation process consists of seven steps:

The packet capture was processed by the Flowmon flow exporter (https://www.flowmon.com) to obtain primary flow data containing information from TLS and HTTP protocols.

Additional statistical features were extracted using GoFlows flow exporter (https://github.com/CN-TU/go-flows).

The primary flows were filtered to remove incomplete records and network scans.

The flows from both exporters were merged together into records containing fields from both sources.

Web logs were filtered to cover the same time frame as the flow records.

Web logs were paired with the flow records based on shared properties (IP address, port, time).

The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool ua-parser (https://github.com/ua-parser/uap-python). We replaced the unstructured User-Agent string in the records with the resulting OS.

The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:

basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;

IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;

TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;

HTTP - HTTP Request Host;URL;

User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;

TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;

Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;

ICMP - ICMP TYPE;

The details of OS distribution grouped by the OS family are summarized in the table below. The Other OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.

    OS Family
    Number of flows




    Other
    42474


    Windows
    40349


    Android
    10290


    iOS
    8840


    Mac OS X
    5324


    Linux
    1589


    Ubuntu
    653


    Fedora
    88


    Chrome OS
    53


    Symbian OS
    1


    Slackware
    1


    Linux Mint
    1

Analysis of ‘Mobile Phones Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/artempozdniakov/ukrainian-market-mobile-phones-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

The dataset set contains data about the mobile phones which were released in past 4 years and which can be bought in Ukraine. Dataset contains the model name, brand name and operating system of the phone and it's popularity. It also has it's financial characteristics like lowest/highest/best price and sellers amount. And some of the characteristics like screen/battery size, memory amount and release date. This data can be useful for improving your machine learning, analysis and vizualization, missing data filling skills. I'm waiting for your notebooks! :) Good luck!

--- Original source retains full ownership of the source dataset ---

Oman Internet Usage: Operating System Market Share: Mobile: Fixed-Auto data was reported at 0.000 % in 28 Jul 2024. This records a decrease from the previous number of 0.040 % for 27 Jul 2024. Oman Internet Usage: Operating System Market Share: Mobile: Fixed-Auto data is updated daily, averaging 0.000 % from Apr 2024 (Median) to 28 Jul 2024, with 20 observations. The data reached an all-time high of 0.050 % in 18 Jun 2024 and a record low of 0.000 % in 28 Jul 2024. Oman Internet Usage: Operating System Market Share: Mobile: Fixed-Auto data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Oman – Table OM.SC.IU: Internet Usage: Operating System Market Share.

Cambodia Internet Usage: Operating System Market Share: Mobile: Tizen data was reported at 0.000 % in 04 Dec 2024. This stayed constant from the previous number of 0.000 % for 03 Dec 2024. Cambodia Internet Usage: Operating System Market Share: Mobile: Tizen data is updated daily, averaging 0.010 % from Nov 2024 (Median) to 04 Dec 2024, with 15 observations. The data reached an all-time high of 0.040 % in 28 Nov 2024 and a record low of 0.000 % in 04 Dec 2024. Cambodia Internet Usage: Operating System Market Share: Mobile: Tizen data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Cambodia – Table KH.SC.IU: Internet Usage: Operating System Market Share.

Introduction

Android is one of the most used mobile operating systems worldwide. Due to its technological impact, its open-source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android´s vulnerabilities point to the importance of continuing the development of methods and frameworks to improve its security.

To prevent malware attacks, researches and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.

In this work, we propose to consider some network layer features as the basis for machine learning models that can successfully detect malware applications, using open datasets from the research community.

Content

This dataset is based on another dataset (DroidCollector) where you can get all the network traffic in pcap files, in our research we preprocessed the files in order to get network features that are illustrated in the next article:

López, C. C. U., Villarreal, J. S. D., Belalcazar, A. F. P., Cadavid, A. N., & Cely, J. G. D. (2018, May). Features to Detect Android Malware. In 2018 IEEE Colombian Conference on Communications and Computing (COLCOM) (pp. 1-6). IEEE.

Acknowledgements

Cao, D., Wang, S., Li, Q., Cheny, Z., Yan, Q., Peng, L., & Yang, B. (2016, August). DroidCollector: A High Performance Framework for High Quality Android Traffic Collection. In Trustcom/BigDataSE/I SPA, 2016 IEEE (pp. 1753-1758). IEEE

Oman Internet Usage: Operating System Market Share: Mobile: Tizen data was reported at 0.000 % in 04 May 2025. This stayed constant from the previous number of 0.000 % for 03 May 2025. Oman Internet Usage: Operating System Market Share: Mobile: Tizen data is updated daily, averaging 0.000 % from Apr 2024 (Median) to 04 May 2025, with 46 observations. The data reached an all-time high of 0.060 % in 31 Mar 2025 and a record low of 0.000 % in 04 May 2025. Oman Internet Usage: Operating System Market Share: Mobile: Tizen data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Oman – Table OM.SC.IU: Internet Usage: Operating System Market Share.

Mobile Sessions by Operating System from Google Analytics. By Month. 2u47-byfn

The demand for mobile coverage with adequate signal quality has triggered criticism due to the maturity of the Internet's diffusion in today's society. However, with the deployment of 5G networks, even 5G NSA by 4G LTE, the complexity of the operating environment of mobile networks has increased. To evaluate the behavior of mobile networks in terms of signal quality and other important metrics for mobile telephony, we developed a dataset consisting of 33 radio parameters that can collect up to 736,974 records generated daily by smartphones and tablets. To create the dataset, an application was designed for the Android operating system using the Kotlin programming language, which can collect data in real time and generate a CSV file. The dataset has 10 samples collected from 9 cities located on the Amazon and Negro Rivers. The complete database covering all regions has 33 columns and 736,974 rows. In addition to the primary dataset, we divided the data into three regions: the metropolitan area of Manaus, the middle Solimões River, and the middle Amazonas River. During the scheduled trips, data were collected along rivers and roads that provide access to the locations selected for the experiment. The data was processed, indexed, and organized into a comprehensive database, then categorized by location. This organization allows experiments using the entire dataset across all cities or with data specific to an individual city. To access the database and conduct initial experiments, Python scripts were developed alongside the database to facilitate data loading and the generation of histograms and charts necessary for the initial investigation. In addition to the graph generation scripts, we also created heat maps based on the collected network variables.The data is organized in a folder named “network_dataset,” which contains a list of datasets. Each dataset is named according to the device ID concatenated with the timestamp at which it was collected.The raw dataset was stored inside the mobile device, and stored in the cloud after the preprocessing steps. The collected data contains mobile network variables such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal-to-Noise Ratio (SNR) and Channel Quality Indicator (CQI), collected in real-time and stored on the mobile device in Comma-separated Values ​​(CSV) data format. After completing the daily collection, the device automatically sends the file to the cloud.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications’ endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user’s privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

Get data on the Verify Ontario mobile application and Ontario's enhanced vaccine certificate. Data includes: * Approximate daily and cumulative Verify Ontario downloads by mobile operating system. * Approximate daily Verify Ontario scan results. * Aggregate daily and cumulative enhanced vaccine certificate downloads. This dataset is subject to change. As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. Learn more about the Verify Ontario mobile application. Learn more about the enhanced vaccine certificate.

This data demonstrates the use of a microcontroller running ROS serial code and wifi chip to bridge a simple miniature mobile robot using serial, allowing it to interact with ROS without the need for a full computer on board. This new methodology enables small robots to be equal players when using high level functionalities of the Robot Operating System (ROS).