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

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 through the collection data from features while using the Android operating system. This article describes a database generated from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 10,517,165 of records related to dynamic, background, app list and static data. Device records were collected every day 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.

Android Games

Games released for the android os

About this dataset

This dataset contains a list of all current Android games, as well as some basic information about each game. This dataset would be of particular interest to researchers who are interested in the Android gaming market, or in game development for Android. The dataset includes information on the game's title, developer (s), publisher (s), genre, release date, and a reference to the game's page on the Android Games Wiki

How to use the dataset

This dataset can be used to research the Android gaming market, or to develop Android games. The data includes the names of 224 current Android games, as well as information about each game's developer, publisher, genre, and release date

Research Ideas

• Analyzing the Android gaming market
• Identifying trends in Android game development
• Recommending new Android games to players

Acknowledgements

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------| | Title | The title of the Android game. (String) | | Developer(s) | The developer(s) of the Android game. (String) | | Publisher(s) | The publisher(s) of the Android game. (String) | | Genre | The genre of the Android game. (String) | | Release date | The release date of the Android game. (String) | | ref | A reference to the game's page on the Android Games Wiki. (String) |

File: df_4.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_3.csv

File: df_2.csv

File: df_6.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_5.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

📄 Dataset Description: Smartprix Mobile Dataset Title: Smartprix Mobile Dataset Source: https://www.smartprix.com/mobiles License: Apache 2.0

📝 Overview This dataset contains detailed specifications of mobile phones scraped from Smartprix — a popular product comparison site. The data includes technical specifications, pricing, and ratings for a wide range of mobile devices available in the Indian market.

📦 Columns Included Column Name Description Name Full name of the smartphone Price Price in Indian Rupees (INR) Rating User rating (if available) SIM SIM type (Dual/Single SIM, 4G/5G supported) Processor Chipset used in the phone (e.g., Snapdragon 8 Gen 1) RAM Amount of RAM Battery Battery capacity (e.g., 5000 mAh) Display Display size and resolution Camera Primary camera specification Card Expandable storage support (e.g., microSD) OS Operating system (e.g., Android 13)

📊 Potential Use Cases Market trend analysis for mobile phones

Feature comparison across brands

Recommendation systems for e-commerce

NLP applications (e.g., generating product summaries)

⚠️ Disclaimer This dataset was collected using publicly available data from Smartprix.com and is intended for educational and research purposes only. Ensure compliance with the source’s terms of service before commercial use.

😊If You downloaded this dataset or it is useful to You, please upvote it!

Description:

This dataset contains records of events and errors related to the operation of mobile applications on various mobile devices. Each entry includes information about the timestamp, device characteristics, session identifiers, and textual descriptions of events or errors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F72a315b39866c02162b229d5a209f4b4%2F5.png?generation=1695227457850330&alt=media" alt=""> Data Fields: - Status: A numerical indicator of the event status (e.g., 0 for success, 1 for error). - Event: A textual description of the action or event, including error text if an error occurred. - Device Identification: Information about the mobile device, including model and Android version. - App Version: The version of the mobile application experiencing the event. - App Language: The language in which the application is running. - Android Version: The version of the Android operating system on the device. - Session Identifiers: Unique session or device identifiers associated with the event. - Additional Data: Additional event details, such as the country and other characteristics. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Fbca8f9b9fb8288e258a59fad5e53ac15%2F4.png?generation=1695227273200372&alt=media" alt="">

Protection against malware is particularly relevant on systems running the Android operating system, due to its huge use base and, therefore, its potential for monetization from the attackers.

Protection against malware is particularly relevant in systems running the Android operating system, due to its huge users’ base and, therefore, its potential for monetization from the attackers.

Dynamic malware detection has been widely adopted by the scientific community but not yet in practical applications.

We release DYNAMISM (Dynamic Analysis of Malware), a dataset containing execution traces of both benign and malicious applications running on Android OS, in order to facilitate further research as well as to facilitate the adoption of dynamic detection in practice. The dataset contains execution traces from 2,386 benign applications and 2,495 malicious applications taken from the Malware Genome Project repository [http://www.malgenomeproject.org] and from Drebin Dataset [https://www.sec.cs.tu-bs.de/~danarp/drebin/]. Execution records were obtained by running the applications, one at a time, on the Android emulator. For each application, a maximum of 2,000 stimuli were applied with a maximum execution time of 10 minutes. For most of the applications, all the stimuli could be applied in this timeframe. In some of the traces none of the two limits is reached due to emulator hiccups. Collected features are related to the memory and CPU usage, network interaction and system calls and their monitoring is performed with a period of two seconds. The Android emulator of the Android Software Development Kit for Android 4.0 (release 20140702) was used. To guarantee that the system was always in a mint condition when a new sample is started, thus avoiding possible interference (e.g., changed settings, running processes, and modifications of the operating system files) from previously run samples, the Android operating system was each time re-initialized before running each application. The application execution process was automated by means of a shell script that made use of Android Debug Bridge (adb) and that was run on a Linux PC. The Monkey application exerciser was used in the script as a generator of the aforementioned stimuli. The Monkey is a command-line tool that can be run on any emulator instance or on a device; it sends a pseudo-random stream of user events (stimuli) into the system, which acts as a stress test on the application software.

In this dataset, we provide both per-app CSV files as well as unified files, in which CSV files of single applications have been concatenated. The CSV files contain the features extracted from the raw execution record. The provided files are listed below:

• benign-per_app-csv.zip - features obtained by executing benign applications, one CSV per application

• benign-unified-csv.zip - features obtained by executing benign applications, only one CSV file

• malicious-per_app-csv.zip - features obtained by executing malicious applications, one CSV per application

• malicious-unified-csv.zip - features obtained by executing malicious applications, only one CSV file

Context

I had a dataset of about 202 million Android smartphone logs (from +14.000 users) at my disposal which we had to contextualize for academic research purposes. Since the database contained the name of the app as registered on the Android phone (e.g. com.nianticlabs.pokemongo), it was relatively easy to build a scraper to collect some additional info on the apps (e.g. genre of app, permissions of app, etc.). In total, I scraped metadata on more than 50.000 apps.

The difference between other available app datasets (on Kaggle) is that:

1. The scraper collected data from five different platforms, not just Google Play. This decreased the negative impact legacy versions and discarded apps had on the amount of missings in the final dataset. The scraper took on a sequential scraping strategy, meaning that it started its search on the Play Store and sequentially looked on other platforms if the app was not available on the Google platform. All app categories are harmonized with the Google Play categories functioning as the gold standard.

2. I performed an extensive automated and manual quality check of the data obtained from these repositories (see 'content' paragraph). Although some of these checks are relatively automated (e.g. fuzzymatching), the most laborious check involved ranking the apps by popularity among the users in the database and looking for inconsistencies. For example, both legitimate sport apps (e.g. Strava) and sport games (e.g. FIFA) are categorized in Google Play as 'sports'. For this reason, I created an additional 'sport games' category. Another example would be the creation of a separate dating-app category; as these apps are officially categorized as a "lifestyle" (or sometimes "social") app, which is not only inconsistent but above all vague. The new category column is the end result of this manual check.

Given both the sequential scraping strategy and the multiple data quality checks performed, this is probably one of the most valid and extensive Android app datasets out there.

Obviously, some variables were not available on some of the platforms. Here's a quick overview of the variables, including an indication of whether this specific parameter was available on the platform:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2F5ce86f6ab8bb43145df08255d76e5a3f%2Fapp%20scraper%20variables.PNG?generation=1597328745448228&alt=media" alt="">

Around 2000 apps were not found in any repository, but are still included in the dataset (indicated by the "not found in databases" string in multiple columns).

The .csv file is the original file format of the dataset, but since dealing with csv files is probably a major cause of anger fits among data analysts around the globe, I also included an Excel version of the file just in case.

If you would like to use this dataset for your own research, but you're afraid the reviewers will question the performed 'manual check', just cite one of these (or both) papers:

• The extended abstract on the development of the scraper, presented at the ICA 2020 conference

Boghe, K., De Grove, F., Herrewijn, L., & De Marez, L. (2020). Scraping application data from the web— Addressing the temporality of online repositories when working with trace data. Extended abstract presented at the 70th International Communication Association Conference

• A publication where we use the dataset presented here

Boghe, K., Herrewijn, L., De Grove, F., Van Gaeveren, K., & De Marez, L. (2020). Exploring the effect of in-game purchases on mobile game use with smartphone trace data. Media and Communication,8(3). doi: 10.17645/mac.v8i3.3007

Citing these two references will probably (and hopefully) serve as some kind of previous validation/'vetting' for your reviewers.

Content

Since I wrote an extended abstract based on my experience with writing the scraper, I'll just shamelessly copy/paste a couple of paragraphs from said abstract to provide some additional info here.

"One of the main objectives of our scraper was to deal with the inherent temporality of web data and app marketplaces. Not only do apps gradually disappear from depositories, but subtle name changes and the existence of legacy versions complicate matters further. While Google Play serves as the golden standard for Android applications, the information-value of this repository diminishes rapidly as the age of the historical data increases. For this reason, we go beyond Google’s Play Store and additionally use alternative repositories. Such repositories are often less well-maintained and thus contain information...

This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the International Conference on Mobile Software Engineering and Systems (MOBILESoft) in 2015.

Dataset Collection We built a dataset that consists of a random sample of Android app metadata and user reviews available on the Google Play Store on January and March 2014. Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice. The dataset D1 contains available apps in the Google Play Store in January 2014. Then, we created a new snapshot (D2) of the Google Play Store in March 2014.

The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.

For each app, we retrieved the following metadata: name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting, and the set of permission requests.

In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata: title, description, device, and version of the app. None of these fields were mandatory, thus several reviews lack some of these details. From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).

Dataset Stats Some stats about the datasets:

• D1 (Jan. 2014) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.

• D2 (Mar. 2014) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.

Additional stats about the datasets are available here.

Dataset Description To store the dataset, we created a graph database with Neo4j. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g., clusters of apps sharing similar sets of permission requests).

In particular, our dataset graph contains six types of nodes: - APP nodes containing metadata of each app, - PERMISSION nodes describing permission types, - CATEGORY nodes describing app categories, - SUBCATEGORY nodes describing app subcategories, - USER_REVIEW nodes storing user reviews. - TOPIC topics mined from user reviews (using LDA).

Furthermore, there are five types of relationships between APP nodes and each of the remaining nodes:

• USES_PERMISSION relationships between APP and PERMISSION nodes
• HAS_REVIEW between APP and USER_REVIEW nodes
• HAS_TOPIC between USER_REVIEW and TOPIC nodes
• BELONGS_TO_CATEGORY between APP and CATEGORY nodes
• BELONGS_TO_SUBCATEGORY between APP and SUBCATEGORY nodes

Dataset Files Info

Neo4j 2.0 Databases

googlePlayDB1-Jan2014_neo4j_2_0.rar

googlePlayDB2-Mar2014_neo4j_2_0.rar We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with Neo4j 2.0. In particular with the tool version 'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).

Neo4j 3.5 Databases

googlePlayDB1-Jan2014_neo4j_3_5_28.rar

googlePlayDB2-Mar2014_neo4j_3_5_28.rar Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official Neo4j Download Center. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28. The databases can be opened with the tool version: 'Neo4j Community Edition 3.5.28'. The tool can be downloaded from the official Neo4j Donwload page.

  In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the Neo4j Migration Guide.

  First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j

A large-scale dataset on the dynamic profiles based on function calls of 35,974 benign and malicious Android apps from 10 historical years (2010 through 2019). Function calls are a commonly used means to model program behaviors, which may contribute to various code analysis approaches to assuring software correctness, reliability, and security. In particular, our dataset includes dynamic profiles of each app resulting from the same-length of time (10 mins) of being exercised by randomly generated inputs on both emulator and real device, enabling interesting and useful app analysis that reason about app behaviors in an evolutionary perspective while informing the differences of app behaviors on different run-time hardware platforms. Since we have 20 yearly datasets associated with 35,974 unique Android apps across the 10 years, profiling these apps took 12,000 hours. Considering the costs of filtering out apps that were originally sampled but that we were unable to profile (due to various reasons such as broken APKs, not being executable because of incompatibility issues, not instrumentable, etc.), we took over two years to produce all these traces. We hope to save future researchers' time in producing such a set of dynamic data to enable their empirical and technical work.

==================

Thanks for your interest in our dataset. Collecting this dataset took tremendous computational and human effort. Thus, please observe the following restrictions in using our dataset:

- Do not redistribute this dataset without our consent.
- Do not make commercial usage of this dataset.
- Get a faculty, or someone in a permanent position, to agree and commit to these conditions.
- When publishing your work that uses our dataset, please cite the following MSR 2021 data paper.

@inproceedings{AndroidCT,
title = {AndroCT: Ten Years of App Call Traces in Android},
author = {Wen Li, Xiaoqin Fu, and Haipeng Cai},
booktitle = {The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track},
year = {2021},
}

Android Views

## Overview

Android Views is a dataset for object detection tasks - it contains Android UI Views annotations for 1,600 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains 18,850 normal android application packages and 10,000 malware android packages which are used to identify the behaviour of malware application on permission they need at run-time.

Title: System Call Traces from Real and Synthetic Sources

Description: This dataset comprises a collection of system call procedure traces collected across various devices and environments. It includes both real-world system call sequences (captured from actual android operating systems) and synthetically generated sequences designed to simulate realistic system behavior.

The data is structured to support a range of use cases, including:

Intrusion detection systems Anomaly detection Behavioral profiling of applications

The dataset is ideal for training and evaluating machine learning models that require low-level OS interaction data. By including both real and synthetic traces, it allows for balanced experimentation in controlled and uncontrolled conditions.

Features:

Real system call traces from multiple devices Synthetic traces designed to mimic real patterns Labelled for supervised learning tasks (if applicable) Suitable for time-series, classification, or sequence modeling

Intended Use: This dataset can be used in academic research, cybersecurity benchmarking, and development of intelligent systems call analysis tools.

This dataset consists of apps needed permissions during installation and run-time. We collect apps from three different sources google play, third-party apps and malware dataset. This file contains more than 5,00,000 Android apps. features extracted at the time of installation and execution. One file contains the name of the features and others contain .apk file corresponding to it extracted permissions and API calls. Benign apps are collected from Google's play store, hiapk, app china, Android, mumayi , gfan slideme, and pandaapp. These .apk files collected from the last three years continuously and contain 81 distinct malware families.

The Android Mischief Dataset is a dataset of network traffic from mobile phones infected with Android RATs. Its goal is to offer the community a dataset to learn and analyze the network behavior of RATs to propose new detections to protect our devices.

The dataset consists of 8 packet captures from 8 executed Android RATs. The Android RATs used in the dataset are: - RAT01 - Android Tester v6.4.6 - RAT02 - DroidJack v4.4 - RAT03 - HawkShaw - RAT04 - SpyMAX v2.0 - RAT05 - AndroRAT - RAT06 - Saefko Attack Systems v4.9 - RAT07 - AhMyth - RAT08 - Command-line AndroRAT

The dataset contains a folder and its zip for each of the experiments. Each experiment was conducted manually by controlling the attacker and the victim. Considering that, each folder contains the following files:

• README.md - the generic description of the execution, containing the name of the executed RAT, details of the RAT execution environment, details of the pcap (client’s IP and server’s IP, time of start of the infection).
• APK - APK file generated by the RAT’s attacker program.
• Log - very detailed and specific time log of all the actions performed in the client and the server during the experiment.
• Pcap - network traffic of the whole infection.
• Screenshots - a folder with screenshots of the mobile device and controller while performing malicious actions.
• Zeek logs - a folder with Zeek generated logs after running Zeek on a RAT pcap.

The zip files are encrypted with the password ‘infected’.

Dataset consisting of feature vectors of 215 attributes extracted from 15,036 applications (5,560 malware apps from Drebin project and 9,476 benign apps). The dataset has been used to develop and evaluate multilevel classifier fusion approach for Android malware detection, published in the IEEE Transactions on Cybernetics paper 'DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection'. The supporting file contains further description of the feature vectors/attributes obtained via static code analysis of the Android apps.

We present the dataset for the article "Snapshots of Daily Life: Situations Investigated Through the Lens of Smartphone Sensing." The data were collected as part of the Smartphone Sensing Panel Study and include 9,790 situational snapshots (observations) from N = 455 participants collected over 14 days of daily life using mobile sensing and experience sampling. Specifically, Dataset 1 is an aggregated mobile sensing dataset with 1,365 cues (including variables extracted from GPS, phone, app, activity logs, etc.) and experience sampling on situational awareness and affective valence. Dataset 2 contains the Big Five as person variables. Demographic and technical variables (age, gender, education, manufacturer, and Android version of the smartphone) that were not used for the data analyses were removed for privacy reasons, but can be made available upon request. The datasets are documented by a comprehensive accompanying codebook. Additional materials (e.g., preprocessing and analysis code) can also be found at https://osf.io/b7krz/. Further details on the variables provided and the associated study procedures can be found in the journal article: Schoedel, R., Kunz, F., Bergmann, M., Bemmann, F., Bühner, M., & Sust, L. (Accepted). Snapshots of Daily Life: Situations Investigated Through the Lens of Smartphone Sensing. Accepted for Publication in: Journal of Personality and Social Psychology. Dataset for: Schoedel, R., Kunz, F., Bergmann, M., Bemmann, F., Bühner, M., & Sust, L. (2023). Snapshots of daily life: Situations investigated through the lens of smartphone sensing. Journal of Personality and Social Psychology. Advance online publication. https://doi.org/10.1037/pspp0000469

We present the dataset for the article "Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies." The data were collected as part of the Smartphone Sensing Panel Study and include more than 25,000 observations from N = 592 participants collected over two 14-day experience sampling periods that included continuous mobile sensing. Demographic and technical variables (e.g., smartphone manufacturer, and Android version of the smartphone) that were not used for the data analyses were removed for privacy reasons. The datasets are documented by a comprehensive accompanying codebook. Additional materials (e.g., preprocessing and analysis code) can also be found at https://osf.io/jw3bn/. Further details on the variables provided and the associated study procedures can be found in the journal article: Reiter, T., & Schoedel, R. (2023). Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies. Behavior Research Methods. Advance online publication. https://doi.org/10.3758/s13428-023-02252-9

This is the dataset and models used in the paper entitled "Translating Video Recordings of Mobile App Usages into Replayable Scenarios" published at the 42nd International Conference on Software Engineering (ICSE'20)

Link to V2S Paper: https://arxiv.org/abs/2005.09057

Android UI Dataset

## Overview

Android UI Dataset is a dataset for object detection tasks - it contains Icons Buttons ImageButton annotations for 6,192 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

This is the dataset used to train and evaluate the CNN and KNN machine learning techniques for the ReDraw paper, published in IEEE Transactions on Software Engineering in 2018.

Link to ReDraw Paper: https://arxiv.org/abs/1802.02312