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84 results for “android”
MaDroid: A Maliciousness-aware Multifeatured Dataset for Detecting Android Malware
<p>MaDroid is a maliciousness-aware multifeatured dataset of system calls focused on APK anomaly detection. The dataset includes 50,429 well-marked normal and abnormal system call sequences, with 24,789 and 25,640 sets of normal and abnormal sequences, respectively, for a total of 1.1 billion system call feature information. Each APK is labeled with the latest VT checksum information, and the sequence data includes 81 groups of system calls, system call parameters, and return values. The size of the whole dataset is 457 GB (19 GB after compression), including 236 GB of malicious system call sequence data. The APKs from which the system call feature sequences are derived cover mobile apps of different types released at different times in the past 14 years (2010-2023), covering 10 mainstream app markets, including Google Play, PlayDrone, Anzhi, etc. The APKs are also used as the source of the system call feature sequences, and the system call sequence data is used as the source of the APKs. anzhi, etc. We store the source code and dataset in two open platforms, GitHub and Zenodo, respectively.</p><h2>DataSet</h2><p>Release address: <a href="http://doi.org/10.5281/zenodo.7997398">http://doi.org/10.5281/zenodo.7997398</a></p><ul><li>The dataset consists of two classifications, Normal and Malware, with a total of 21 zip files. The installation files of each sequence come from 10 application markets such as Google Play, PlayDrone, Anzhi, etc. The Malware classification contains information about the running system call sequences of some APKs in the Drebin dataset.</li><li>RF, MLP and GBDT models were used to establish benchmarks for the dataset, the use of the models can be found in the source code.</li><li>The file `merge_all_csv_count_online_check_replenish.csv` is the dataset APK information. We provide APK name (SHA256 name for APK only), classification, APK capacity, number of sequences, log capacity, CVT, OVT value, check time, etc.</li></ul><h2>Source Code</h2><p>Release address: <a href="https://github.com/HNUSystemsLab/MaDroid">https://github.com/HNUSystemsLab/MaDroid</a></p><ul><li>The released source code contains two folders, `Source_Code` and `ml_metadata`. Where `Source_Code` is the automated framework for data collection, the tool chain and some notes on the structure of the source files. `ml_metadata` contains the metadata used for machine learning, the partitioned data on which the article builds its benchmark.</li><li>The automation framework is described in detail in the `Readme.md` document in the `Source_Code` directory. It consists of four main parts: environment requirements, program structure, quick start (working steps), model training and evaluation (including training and evaluation). It describes in detail the preparation of the environment, the data import method, the functional description of each file in the source code directory, the working principle of model training and evaluation, and other related contents.</li></ul><h2>Tips: </h2><ul><li>MAS is another name of MaDroid, the content shown here is the final version of "Readme.md".</li><li>A Large-scale Multi-feature Dataset for Anomaly Detection of Mobile Applications, which is the name of the document during our experiment.</li></ul>
Taxonomy of Security Weaknesses in Java and Kotlin Android Apps
<p>This is the replication package for the paper "Taxonomy of security weaknesses in Java and Kotlin Android apps" accepted for inclusion in The Journal of Systems & Software</p>
SAST database of repository Luca App Android
<p>SAST database as sqlite database containing the<a href="https://gitlab.com/lucaapp/android"> LucaApp Android Gitlab repository</a>.</p> <p>Retrieved on June 21<sup>nd</sup>, 2021 using our SAST analysis pipeline.</p>
Multi-Sensor Dataset From Android Smart Devices
<p>This dataset contains data acquired on various Android devices, using an Android app called ''Mimir'', developed by the authors. Focus is given on raw GNSS measurements, but other sensors are also logged in the surveys. The dataset is provided under the CC-BY 4.0 license. More information are provided inside the ''readme.md'' provided along the dataset, as well as in our related publication.</p>
Artefacts for ICSE 2021 technical paper: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"
<p>This repository represents our artefacts to replicate our paper which is in the proceedings of ICSE 2021: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"</p>
Evaluation of Android Malware Detection Based on System Calls - Dataset
<p>This is a data set accompanying a paper "Evaluation of Android Malware Detection Based on System Calls" published in the proceedings of the International Workshop on Security and Privacy Analytics, New Orleans, USA, 2016.</p> <p>See README.md for details of the data set.</p>
A Dataset of Atoms of Confusion in the Android Open Source Project
<p>This is a dataset cataloging the presence of Atoms of Confusion in the Android Open Source Project, alongside metrics<br>of object-oriented software development.</p> <p> </p> <h3>Schema</h3> <h4>ac_reports</h4> <p><strong>id </strong>INTEGER The primary key <br>project_name VARCHAR Name of the project/repository in the AOSP<br>line INTEGER NOT NULL Line where the AC was found<br>snippet VARCHAR NOT NULL The excerpt of code that represents the Atom of Confusion<br>class_name VARCHAR Name of the class where the AC was found<br>ac VARCHAR NOT NULL Type of Atom of Confusion<br>path VARCHAR Path to the file in the AOSP superproject<br>"commit" VARCHAR SHA of the commit of the file in the repository<br>loc VARCHAR Number of lines of code in the file</p> <h4>files</h4> <p>path VARCHAR The path of the file</p> <p>loc INTEGER Lines of code</p> <p>project_name VARCHAR Name of the project/repository in the AOSP</p> <h4>projects</h4> <p>name VARCHAR The name of the project/repository in the AOSP</p> <p> </p> <h4>classes</h4> <p>id INTEGER The primary key </p> <p>file_path VARCHAR Path to the file in the AOSP superproject</p> <p>name VARCHAR Name of the class</p> <p>type VARCHAR The type of class (e.g. interface, anonymous...)</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>dit INTEGER From CK: Depth Inheritance Tree</p> <p>noc INTEGER From CK: Number of Children</p> <p>rfc INTEGER From CK: Response for Class</p> <p>lcom INTEGER From CK: Lack of Cohesion of Methods</p> <p>lcom_normalized DOUBLE From CK: Refers to the LCOM* metric that computes Lack of Cohesion of Methods between 0 and 1.</p> <p>tcc DOUBLE From CK: Tight Class Cohesion</p> <p>lcc DOUBLE From CK: Loose Class Cohesion</p> <p>"totalMethodsQty" INTEGER From CK: Number of methods (constructors are included)</p> <p>"staticMethodsQty" INTEGER From CK: Number of static methods</p> <p>"publicMethodsQty" INTEGER From CK: Number of public methods</p> <p>"privateMethodsQty" INTEGER From CK: Number of private methods</p> <p>"protectedMethodsQty" INTEGER From CK: Number of protected methods</p> <p>"defaultMethodsQty" INTEGER From CK: Number of default methods</p> <p>"visibleMethodsQty" INTEGER From CK: Number of visible methods</p> <p>"abstractMethodsQty" INTEGER From CK: Number of abstract methods</p> <p>"finalMethodsQty" INTEGER From CK: Number of final methods</p> <p>"synchronizedMethodsQty" INTEGER From CK: Number of synchronized methods</p> <p>"totalFieldsQty" INTEGER From CK: Number of fields</p> <p>"staticFieldsQty" INTEGER From CK: Number of static fields</p> <p>"publicFieldsQty" INTEGER From CK: Number of public fields</p> <p>"privateFieldsQty" INTEGER From CK: Number of private fields</p> <p>"protectedFieldsQty" INTEGER From CK: Number of protected fields</p> <p>"defaultFieldsQty" INTEGER From CK: Number of default fields</p> <p>"finalFieldsQty" INTEGER From CK: Number of final fields</p> <p>"synchronizedFieldsQty" INTEGER From CK: Number of synchronized fields</p> <p>nosi INTEGER From CK: Number of static invocations</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p> </p> <h4>methods</h4> <p>id_ INTEGER NOT NULL The primary key</p> <p>file_path VARCHAR NOT NULL Path to the file in the AOSP superproject</p> <p>class_id INTEGER NOT NULL ID of the class where this method is</p> <p>name VARCHAR NOT NULL The name of the method</p> <p>constructor BOOLEAN NOT NULL From CK: True if method is a constructor</p> <p>line INTEGER From CK: Line where this method is located</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>rfc INTEGER From CK: Response for Class</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"parametersQty" INTEGER From CK: Number of parameters</p> <p>"methodsInvokedQty" INTEGER From CK: all methods invoked</p> <p>"methodsInvokedLocalQty" INTEGER From CK: methods invoked locally</p> <p>"methodsInvokedIndirectLocalQty" INTEGER From CK: number of indirect local invocations</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p>"hasJavaDoc" BOOLEAN From CK: True if method has a JavaDoc</p>
AndroCT: Ten Years of App Call Traces in Android
<p>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. </p> <p>==================</p> <p>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: </p> <p> - Do not redistribute this dataset without our consent.<br> - Do not make commercial usage of this dataset.<br> - Get a faculty, or someone in a permanent position, to agree and commit to these conditions.<br> - When publishing your work that uses our dataset, please cite the following MSR 2021 data paper.</p> <p><br> @inproceedings{AndroidCT,<br> title = {AndroCT: Ten Years of App Call Traces in Android},<br> author = {<a href="https://2021.msrconf.org/profile/wenli">Wen Li</a>, <a href="https://2021.msrconf.org/profile/xiaoqinfu">Xiaoqin Fu</a>, and <a href="https://2021.msrconf.org/profile/haipengcai">Haipeng Cai</a>},<br> booktitle = {The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track},<br> year = {2021},<br> }</p>
RANCANG BANGUN GENERATOR PLASMA NON TERMAL PADA BIDANG TEKSTIL SKALA LABORATORIUM DENGAN SISTEM KONTROL ANDROID
<p>Dalam beberapa tahun terakhir, penggunaan fisika plasma dan fisika elektronika-komputasi telah menarik minat riset beberapa peneliti di industri tekstil untuk dapat menerapkan teknologi plasma tersebut pada berbagai proses tekstil. Tujuan dari penelitian ini adalah dihasilkannya suatu alat generator plasma non termal pada bidang tekstil skala laboratorium dengan sistem kontrol android yang berfungsi sebagai piranti untuk memodifikasi sifat fisika dan kimia bahan tekstil (benang dan kain), serta sebagai sterilisasi bahan tekstil dengan proses control otomatis.</p>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
Android Malware Dataset with VirusTotal Labels
<p>This dataset contains labels of 2.47 million Android apk hashes extracted from VirusTotal reports.</p> <p>The dataset was used in the experiments of our publication titled <a href="https://www.mdpi.com/1424-8220/21/16/5671" rel="nofollow">An Analysis of Android Malware Classification Services<br></a><br>The csv of the labels that was extracted from the VirusTotal reports is provided in <code>labeling_dataset.csv.gz</code> . A cell's value of <code>-1</code> is used whenever there was no result from the<br>engine for the given apk file hash value. The column names are provided in <code>cols_labeling_dataset.csv</code>.</p> <p>Note</p> <p><code>-1</code> is a <code>string</code> and not an <code>integer</code></p> <h2>If you use information from this repo, please cite our paper</h2> <p>Rashed M, Suarez-Tangil G. An Analysis of Android Malware Classification Services. Sensors. 2021; 21(16):5671. <a href="https://doi.org/10.3390/s21165671" rel="nofollow">https://doi.org/10.3390/s21165671</a></p> <h2>BibTeX</h2> <p><code>@Article</code>{s21165671,<br>AUTHOR = {Rashed, Mohammed and Suarez-Tangil, Guillermo},<br>TITLE = {An Analysis of Android Malware Classification Services},<br>JOURNAL = {Sensors},<br>VOLUME = {21},<br>YEAR = {2021},<br>NUMBER = {16},<br>ARTICLE-NUMBER = {5671},<br>URL = {<a href="https://www.mdpi.com/1424-8220/21/16/5671%7D,%5C" rel="nofollow">https://www.mdpi.com/1424-8220/21/16/5671},\</a><br>ISSN = {1424-8220},<br>DOI = {10.3390/s21165671}<br>}</p> <h2>Required Software</h2> <p><code>gzip</code></p> <ul> <li>Debian-based Linux: you may install it using the following command <code>apt-get install gzip</code></li> <li>MacOS: <code>gzip</code> is pre-installed</li> <li>Windows: you may download <code>gzip</code> from <a href="http://gnuwin32.sourceforge.net/packages/gzip.htm" rel="nofollow">http://gnuwin32.sourceforge.net/packages/gzip.htm</a></li> </ul> <h2>How to use the file?</h2> <p>There are two ways to use the file:</p> <ol> <li>Extract the gzip file and then you will have a csv output file. For that you need to install gzip and then extracting .csv.gz. The user may use the command <code>gunzip labelingDataset.csv.gz</code></li> <li>Extract information from the zipped file directly (following the same logic of <a href="https://androzoo.uni.lu/lists" rel="nofollow">AndroZoo's csv</a>):<br>To extract the first column and save to a file called <code>list_of_selected_sha256</code>, run the following command:<br><code>zcat labelingDataset.csv.gz | cut -d',' -f1 > list_of_selected_sha256</code><br>To obtain rows of apk hashes that were first seen after the 1st of May, 2016, run this command:<br><code>zcat labeling_dataset.csv.gz | grep -v ',snaggamea' | awk -F, '{if ( $2 >= "2016-05" ) {print} }'</code></li> </ol>
The ReDraw Dataset: A Set of Android Screenshots, GUI Metadata, and Labeled Images of GUI Components
<p>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.</p> <p>Link to ReDraw Paper: https://arxiv.org/abs/1802.02312 </p>
Data and Code for the paper "GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies"
<p>This package contains data and code to replicate the findings presented in our paper titled "<em>GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies</em>".</p> <p><strong>Abstract</strong></p> <p>Graphical User Interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, Exploratory Testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience-based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task, that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the<br> diversity of testers’ performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re-executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step towards filling this gap in the literature, in this work we conduct an empirical evaluation involving four open-source Android apps and twenty masters students, that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a Capture and Replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage, that different-sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.</p> <p> </p> <p><strong>Contents and Instructions</strong></p> <p>This package contains:</p> <ul> <li><strong>apps-under-test.zip</strong> A zip archive containing the source code of the four Android applications we considered in our study, namely MunchLife, TippyTipper, Trolly, and SimplyDo.</li> <li><strong>apps-under-test-instrumented.zip</strong> A zip archive containing the instrumented source code of the four Android applications we used to compute branch coverage.</li> <li><strong>students-test-suites.zip</strong> A zip archive containing the test suites developed by the students using Uninformed Exploratory Testing (referred to as "Black Box" in the subdirectories) and Informed Exploratory Testing (referred to as "White Box" in the subdirectories). This also includes coverage reports.</li> <li><strong>compute-coverage-unions.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate LOC coverage of all possible subsets of students. The scripts have been tested on MS Windows. To compute the LOC coverage achieved by any possible subsets of testers using IET and UET strategies, run the <em>analysisAndReport.py</em> script. To compute the LOC coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>analysisAndReport_UET_IET_combinations_emma.py</em> script.</li> <li><strong>branch-coverage-computation.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate branch coverage of all considered subsets of students. The scripts have been tested on MS Windows. To compute the branch coverage achieved by any possible subsets of testers using UET and I+UET strategies, run the <em>branch_coverage_analysis.py</em> script. To compute the code coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>mixed_branch_coverage_analysis.py</em> script.</li> <li><strong>data-analysis-scripts.zip</strong> A zip archive containing R scripts to merge and manipulate coverage data, to carry out statistical analysis and draw plots. All data concerning RQ1 and RQ2 is available as a ready-to-use R data frame in the <em>./data/all_coverage_data.rds</em> file. All data concerning RQ3 is available in the <em>./data/all_mixed_coverage_data.rds </em>file.</li> </ul>
iWanDroid: Demand-driven Information Flow Analysis of WebView in Android Hybrid Apps
<p>The repo contains the tool iWanDroid: Artifacts for the paper Demand-driven Information Flow Analysis of WebView in Android Hybrid Apps, accepted at ISSRE 2023.</p> <p>Android hybrid apps augment native apps with web and inter-language communication capabilities. These apps facilitate the integration of web components, including JavaScript, into native apps. Besides, they allow a two-way communication where JavaScript can utilize functionality shared by the native side (Java). However, due to operational differences between Java and JavaScript, the semantics of this communication are complex. Tracking information flows via this communication channel, i.e., between these heterogeneous platforms, becomes intricate.</p> <p>Multiple approaches have been proposed to analyze hybrid apps. However, most of them focus on specific classes of web-induced vulnerabilities or provide rudimentary tracking of specific information flows via this communication channel. This work proposes a demand-driven analysis to comprehensively track information flow violations from the native side to JavaScript and vice-versa. To this end, our framework selectively creates data flow summaries of the shared native-side code based on its usage in the corresponding JavaScript code. We demonstrate the efficacy of our approach by applying it to various benchmarks and large-scale apps.</p> <p> </p>
Metrics by commit for Android
<p>This dataset is a set of csv files. Each file contains the metrics by commit of all android code samples analyzed in the paper. </p>
The Impact of Tool Configuration Spaces on the Evaluation of Configurable Taint Analysis for Android
<p>The data accompanying our ISSTA'2021 submission, <em>Rethinking Android Taint Analysis Evaluations: A Study of the Impact of Tool Configuration Spaces</em></p> <p> </p> <p>Structure:</p> <p><em>results</em>: contains the raw results output by AQL for the runs on Fossdroid and Google Play. Note that we wrote DroidBench results directly to CSV, so there are no "raw" results for them. Instead, see the summaries package. The collection of APKs for both datasets are also in this package.</p> <p><em>summaries</em>: contains CSV summaries of the three replications of our experiments on all three datasets (including examples of Amandroid's nondetermism on non-default configurations).</p> <p><em>datasets</em>: contains our FossDroid classified results and justifications. Please see the README.md in that package for more information.</p> <p><em>diagrams: </em>contains the graphs detailing the FlowDroid and DroidSafe configuration spaces, including their partial orders and disablement relationships.</p> <p><em>violations</em>: contains the records of violations of our partial orders.</p>
Data supplementing the conference paper "Who you gonna call? Analyzing web requests in Android applications", 14th International Conference on Mining Software Repositories 2017.
<p>This repository contains the data supplementing the paper:</p> <p>M. Rapoport, P. Suter, E. Wittern, O. Lhótak, J. Dolby, "Who you gonna call? Analyzing web requests in Android applications", MSR 2017.</p> <p>A detailed description of the data is included in the archive in README.md.</p>
Artifacts for Demystifying Device-specific Compatibility Issues in Android apps
<p>This artifact is for the paper "Demystifying Device-specific Compatibility Issues in Android Apps."</p> <p>We include our repositories link, commit hash, our GitHub crawler, its output, and some representative examples for Device-specific compatibility issues in this artifact.</p> <p>Please checkout the README.pdf first. The main artifact pack is the `device-specific-artifacts.zip`.</p> <p>Other zips starting with "repo-" are repositories archives.</p>
PsySuite: Performing multimodal psychophysical testing within the Android environment
<p>Data used for either the hardware or the behavioral validation of the Android APP <em>PsySuite</em>.</p>
Twindroid - System calls traces - Android apps
<p>TwinDroid - a dataset of over 10 000 system calls traces, from both benign and infected Android apps. A large part of the dataset is composed of traces from pairs of benign and infected apps.</p> <p>For more traces you can visit https://zenodo.org/record/6465271#.Yls3F1yZNH4</p> <p>To trace Android application, you can use our script in https://github.com/RaphaelKhoury/automated-apk-tracing</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.