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8 results for “malware detection”
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>
API traces for malware detection
<p>The dataset consists of traces of benign and malware samples. There are approximately 330k traces in the dataset, each meticulously collected and curated for research and analysis purposes, with an uncompressed size of 550GB. The dataset was collected during the second half of 2023. The file "shas_by_families.json" links each SHA(which is also the individual filenames) with the associated malware or benign family. Each file is in json format and includes the parameters of the API call as well.</p> <p> </p> <p>If you are using this dataset, please cite our work on Arxiv.<br>@misc{fellicious2025malwaredetectionbasedapi,<br> title={Malware Detection based on API calls}, <br> author={Christofer Fellicious and Manuel Bischof and Kevin Mayer and Dorian Eikenberg and Stefan Hausotte and Hans P. Reiser and Michael Granitzer},<br> year={2025},<br> eprint={2502.12863},<br> archivePrefix={arXiv},<br> primaryClass={cs.CR},<br> url={https://arxiv.org/abs/2502.12863}, <br>}</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>
Benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model"
<p>This repository contains all benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model". It is safe to download these samples.</p>
(Artifact) Understanding Model Weaknesses: A Path to Strengthening DNN-Based Android Malware Detection
Open the record for dataset details and reuse information.
Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples (Artifact Dataset)
<p>Dataset for Peer-Reviewed Artifact of 'Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples' (NDSS 2025) </p>
Malware dataset from VirusShare used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model"
<p>This repository contains all malicious samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model". It is safe to download these samples <strong>without </strong>running them. Please note that when downloading these samples, <strong>close all </strong>anti-virus software, including Windows Defender.</p>
Enhancing Malware Detection for Android Apps: Detecting Fine-granularity Malicious Components
<p>Please refer to the version v1.</p>
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