Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
16
datasets available to search
ShareScore release 0.9.0
Dataset results
16 results for “obfuscation”
CSI-based Device-free Localization and Obfuscation
<p>This dataset has been used to study CSI-based device-free localization and test the performance of different obfuscation systems in the paper "AntiSense: Standard-compliant CSI obfuscation against unauthorized Wi-Fi sensing." It contains many CSI traces collected in an indoor environment in several conditions. All the data have been collected with commercial Wi-Fi systems using IEEE 802.11ac and 80-MHz-wide channels.</p>
Replication Package for "Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems"
<p>This is the replication package for the doctoral dissertation titled "<em>Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems</em>".</p> <p>The contributions of the dissertation were also integrated into the source code plagiarism detection system <a href="https://github.com/jplag/JPlag/">JPlag</a> to ensure they are widely accessible.</p> <p><strong>Contents Overview:</strong></p> <p>- <strong>Datasets</strong>: the artifacts of the evaluation datasets.<br>- <strong>Raw Results</strong>: the measured results of our evaluation.<br>- <strong>Evaluation Scripts</strong>: the evaluation code for plotting and statistical tests.<br>- <strong>Implementation</strong>: the source code of the JPlag-based implementation and the prebuilt application as a JAR file.<br>- <strong>Other</strong>: additional plots.</p>
Set of obfuscated spam dataset by using LeetSpeak transformations
<p>The usage of LeetSpeak and other text hiding tricks is often used by spammers in the distribution of unsolicited contents. To evaluate deobfuscation techniques and their impact on spam content classification, we preprocessed several popular public datasets to partially obfuscate the text. The datasets transformed are:</p> <ul> <li>YouTube Spam Collection [2, 3] which is available on <a href="https://www.dt.fee.unicamp.br/~tiago/youtubespamcollection/">https://www.dt.fee.unicamp.br/~tiago/youtubespamcollection/</a>.</li> <li>a subset of YouTube Comments [4, 5] which is available on <a href="http://mlg.ucd.ie/yt/">http://mlg.ucd.ie/yt/</a>.</li> <li>CSDMC2010 which is available on <a href="http://csmining.org/index.php/spam-email-datasets-.html">http://csmining.org/index.php/spam-email-datasets-.html</a>.</li> <li>TREC2007 which is available on <a href="https://plg.uwaterloo.ca/~gvcormac/treccorpus07/">https://plg.uwaterloo.ca/~gvcormac/treccorpus07/</a></li> </ul>
Image dataset to train a deep learning model to decode Leetspeak obfuscated characters
<p>The dataset contains an image database (18,981 images) that could be used to train a deep learning model to accurately detect characters. We have successfully used it to create a model that identifies characters encoded using LeetSpeak. The original dataset can be found in the Mondragon Unibertsitatea Repository -- https://gitlab.danz.eus/datasharing/ski4spam</p> <p>The training dataset consists of:</p> <p>- Alphabetic letters (a-z) written using different fonts and styles (regular, cursive, bold, cursive+bold)</p> <p>- Handwritten letters: English handwriting from the Chars74k dataset [2] which is available at http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/.</p>
Obfuscation Revealed: Leveraging Electromagnetic Signals for Obfuscated Malware Classification
<p>Data used in the paper: "Obfuscation Revealed: Leveraging Electromagnetic Signals for<br> Obfuscated Malware Classification". The paper has been accepted at <a href="http://acsac.org/">ACSAC-2021</a>.</p> <p>Two dataset are available:</p> <ul> <li>traces_selected_bandwidth.zip: the extracted bandwidth (40) of spectrograms from the testing dataset to reproduce the classification results presented in the paper,</li> <li>raw_data_reduced_dataset.zip: a reduce set of the raw electromagnetic traces to reproduce the end-to-end process (pre-processing and classification).</li> </ul> <p>Due to storage constraints we did not upload the full datasets, fill free to contact us to get the remaining ones.</p>
Modeling Attack Resistant Strong PUF Exploiting Stagewise Obfuscated Interconnections With Improved Reliability [DATASET and Source Code]
<p>Thanks for your interest in our work!</p> <p>In order to facilitate your assessment and replication, we provides the dataset and source codes (verilog/python model/matlab) of our work (OIPUF) here. </p> <p>By the way, our latest work (SOI PUF and cSOI PUF) published in IEEE TIFS (2024) is based on OIPUF. </p> <blockquote> <p>If you have any questions, please feel free to contact with us: <a href="mailto:chongyaoxu@126.com">chongyaoxu@126.com</a> / <a href="mailto:mklaw@um.edu.mo">mklaw@um.edu.mo</a></p> <p>Full text about OIPUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10103139">https://ieeexplore.ieee.org/document/10103139</a></p> <p>Full text about SOI PUF and cSOI PUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10458688">https://ieeexplore.ieee.org/document/10458688</a></p> <p>Source code and FPGA project of SOI PUF and cSOI PUF can be download from <a href="https://github.com/yg99992/SOI_PUF">https://github.com/yg99992/SOI_PUF</a>. </p> </blockquote> <p> </p> <p>Matlab code</p> <p><code>matlab/Generate_OI_block.m</code><br>This is a matlab manuscript used for generating the verilog code of random OI block.</p> <p><code>matlab/OIPUF_64x4_placement.m</code><br>This is a matlab function used for generating XDC file for constraining the placement of (64,4)-OI block</p> <p><code>matlab/OIPUF_64x8_placement.m</code><br>This is a matlab function used for generating XDC file for constraining the placement of (64,8)-OI block</p> <p><code>matlab/OIPUF_placement_example.m</code><br>An example manuscript used for demonstrating the usage of OIPUF_64x4_placement.m and OIPUF_64x8_placement.m</p> <p> </p> <p>Python code</p> <p><code>python/puf_models.py</code><br>The python models of XOR PUFs and OIPUFs, which can be used to generate CRPs.</p> <p>for example:</p> <pre><code>from puf_models import oi_puf # generate a (64,4)-OIPUF and further use the generated OIPUF to generate 1M CRPs crps, puf_instance = oi_puf.gen_CRPs_PUF(64, 4, 1_000_000) </code></pre> <p> </p> <p><code>python/attack_pypuf.py</code><br>A manuscript used to conduct to ANN attack on XOR PUF and OIPUF ('pypuf' package should be installed correctly).</p> <p> </p> <p>Verilog code</p> <p><code>verilog/OIPUF_64_4/</code><br>All the verilog files of (64, 4)-OIPUF</p> <p><code>verilog/OIPUF_64_8/</code><br>All the verilog files of (64, 8)-OIPUF</p> <p> </p> <p>CRP datasets extracted from FPGA</p> <p>It consists of 13 CRP files (All the CRPs are extracted from FPGA):</p> <p><code>FPGA_CRPs/FPGA3_CHAL_100M.csv</code><br>The 100 million 64-bit challenges</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF0.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF0</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF1.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF1</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF2.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF2</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF3.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF3</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF4.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF4</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF5.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF5</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF0.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF0</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF1.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF1</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF2.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF2</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF3.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF3</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF4.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF4</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF5.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF5</p>
Companion double-blind obfuscated package for "A Toolset for Software Development Analytics"
<p>This is a companion package for the paper ""A Toolset for Software Development Analytics", obfuscated to comply with the rules of double-blind review.</p>
Supplementary Material for "Automated Detection of AI-Obfuscated Plagiarism in Modeling Assignments"
<p>This repository contains additional material supporting the paper titled "Automated Detection of AI-Obfuscated Plagiarism in Modeling Assignments", presented at ICSE 2024 (SEET track).</p> <p>The paper presents a token-based approach for detecting modeling plagiarism. It leverages a novel normalization technique to achieve resilience against common obfuscation attacks.</p> <p>The approach was also integrated into the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a>, thus providing a widely accessible solution.</p> <p><strong>Contents Overview:</strong></p> <ul> <li><strong>Source Code:</strong> The implementation of our approach (contribution 1) based on the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a> (v4.0.0). Note that JPlag is licensed under the GPL-3.0 license.</li> <li><strong>ChatGPT Study:</strong> An exploration of how ChatGPT can be exploited for cheating in modeling assignments.</li> <li><strong>Datasets:</strong> The three datasets of our evaluation based on EMF modeling assignments.</li> <li><strong>Raw Results:</strong> All raw data of our evaluation results, as used in our plots and tables.</li> <li><strong>Demo:</strong> A packaged JAR of our approach's implementation alongside an instruction on how to use it.</li> </ul>
Loki: Hardening Code Obfuscation Against Automated Attacks
<p>Artifact containing binaries and result data produced during our evaluation. Check out the code on Github: https://github.com/RUB-SysSec/loki</p>
Android applications obfuscation analysis
<p>Dataset for the research "Android Software Protection in the Wild: A Survey"</p>
Phylogenomics of piranhas and pacus (Serrasalmidae) uncovers how dietary convergence and parallelism obfuscate traditional morphological taxonomy
<p>The Amazon and neighboring South American river basins harbor the world's most diverse assemblages of freshwater fishes. One of the most prominent South American fish families is the Serrasalmidae (pacus and piranhas), found in nearly every continental basin. Serrasalmids are keystone ecological taxa, being some of the top riverine predators as well as the primary seed dispersers in the flooded forest. Despite their widespread occurrence and notable ecologies, serrasalmid evolutionary history and systematics are controversial. For example, the sister taxon to serrasalmids is contentious, the relationships of major clades within the family are inconsistent across different methodologies, and half of the extant serrasalmid genera are suggested to be non-monophyletic. We analyzed exon capture to reexamine the evolutionary relationships among 63 (of 99) species across all 16 serrasalmid genera and their nearest outgroups, including multiple individuals per species to account for cryptic lineages. To reconstruct the timeline of serrasalmid diversification, we time-calibrated this phylogeny using two different fossil-calibration schemes to account for uncertainty in taxonomy with respect to fossil teeth. Finally, we analyzed diet evolution across the family and comment on associated changes in dentition, highlighting the ecomorphological diversity within serrasalmids. We document widespread non-monophyly of genera within Myleinae, as well as between <em>Serrasalmus</em> and <em>Pristobrycon</em>, and propose that reliance on traits like teeth to distinguish among genera is confounded by ecological homoplasy, especially among herbivorous and omnivorous taxa. We clarify the relationships among all serrasalmid genera, propose new subfamily affiliations, and support hemiodontids as the sister taxon to Serrasalmidae.</p>
Artifacts for the ISSTA 2023 Paper: An Empirical Study on the Effects of Obfuscation on Static Machine Learning-based Malicious JavaScript Detectors
<p>An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors</p> <p>This repository contains the evaluation script and the corresponding data of the ISSTA'23 paper "An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors".</p> <p>Abstract</p> <p>Machine learning is increasingly being applied to malicious JavaScript detection in response to the growing number of Web attacks and the attendant costly manual identification. In practice, to hide their malicious behaviors or protect intellectual copyrights, both malicious and benign scripts tend to obfuscate their own code before uploading. While obfuscation is beneficial, it also introduces some additional code features (e.g., dead code) into the code. When machine learning is employed to learn a malicious JavaScript detector, these additional features can affect the model to make it less effective. However, there is still a lack of clear understanding of how robust existing machine learning-based detectors are on different obfuscators.</p> <p>In this paper, we conduct the first empirical study to figure out how obfuscation affects machine learning detectors based on static features. Through the results, we observe several findings: 1) Obfuscation has a significant impact on the effectiveness of detectors, causing an increase both in false negative rate (FNR) and false positive rate (FPR), and the bias of obfuscation in the training set induces detectors to detect obfuscation rather than malicious behaviors. 2) The common measures such as improving the quality of the training set by adding relevant obfuscated samples and leveraging state-of-the-art deep learning models can not work well. 3) The root cause of obfuscation effects on these detectors is that feature spaces they use can only reflect shallow differences in code, not about the nature of benign and malicious, which can be easily affected by the differences brought by obfuscation. 4) Obfuscation has a similar effect on realistic detectors in VirusTotal, indicating<br>that this is a common real-world problem.</p> <p>Getting Started</p> <p>Requirements</p> <pre>install python3 version 3.9.12 pip3 install -r requirements.txt install nodejs install npm npm install escodegen npm install esprima</pre> <p>Step 1: Generating PDGs for JStap</p> <p><code>cd detectors/jstap/pdg_generation</code></p> <p><code>python generate_PDGs.py</code></p> <p>Step 2: Getting the results for RQ1: What Impact Does Obfuscation Have on Static Machine Learning Malicious JavaScript Detectors?</p> <p><code>cd RQ1/</code></p> <p>1. Detectors Performance on Obfuscated Samples.</p> <p>To train the models:</p> <p><code>python RQ1_1_train.py</code></p> <p>To get the results:</p> <p><code>python RQ1_1_test.py</code></p> <p>2. Different Machine Learning Algorithms.</p> <p>To train the models:</p> <p><code>python RQ1_2_train.py</code></p> <p>To get the results:</p> <p><code>python RQ1_2_test.py</code></p> <p>3. Biased Training Sets</p> <p>To train the models:</p> <p><code>python RQ1_3_train.py</code></p> <p>To get the results:</p> <p><code>python RQ1_3_test.py</code></p> <p>All the trained models will be stored in RQ1/models/.</p> <p>All the results will be stored in RQ2/results/.</p> <p>Step 3: Getting the results for RQ2: Are the Common Measures to Mitigate the Impact of Obfuscation Effective?</p> <p><code>cd RQ2/</code></p> <p>1. Training and Testing Detectors on Samples with Same Types of Obfuscation.</p> <p>To train the models:</p> <p><code>python RQ2_1_train.py</code></p> <p>To get the results:</p> <p><code>python RQ2_1_test.py</code></p> <p>2. Training and Testing Detectors on Samples with Different Types of Obfuscation.</p> <p>If you follow the steps, the models is already trained.</p> <p>To get the results:</p> <p><code>python RQ2_2_test.py</code></p> <p>3. BERT Variants.</p> <p>To get the results:</p> <p><code>python RQ2_3.py</code></p> <p>All the trained models will be stored in RQ2/models/.</p> <p>All the results will be stored in RQ2/results/.</p> <p> </p> <p>Step 4: Getting the results for RQ3: What Is the Root Cause of Obfuscation Affecting Static Machine Learning Malicious JavaScript Detectors?</p> <p>To get the results of vectors visualization, top ten features, and distances between vectors sets:</p> <p><code>cd RQ3</code></p> <p><code>python visulization.py</code></p> <p>The figures of vectors visualization will be stored in RQ3/results/.</p> <p>Other results will be shown in the console.</p> <p> </p> <p>Step 5: Getting the results for RQ4: How Does Obfuscation Affect Real-world Static Malicious JavaScript Detectors?</p> <p>To get the results, submit the sample under the folder samples/ to <a href="https://www.virustotal.com/gui/home/upload">VirusTotal</a> .</p> <p> </p> <p>Detailed Instructions</p> <p>detectors</p> <p>The detectors under the folder <code>detectors/</code> are the main projects to be evaluated in our paper, which are <strong>CUJO</strong>, <strong>ZOZZLE</strong>, <strong>JAST</strong>, and <strong>JSTAP</strong>.</p> <p>Detailed setup and usage instructions are described in <code>README.md</code> in the corresponding folder.</p> <p>samples</p> <p>The files under the folder <code>samples/</code> are the samples from a random tenth of our dataset used in our paper.</p> <p>Results can be obtained quickly using these samples. These results will not be exactly the same as in the paper, but they are similar.</p> <p>RQ1</p> <p>The code under folder <code>RQ1/</code> is to figure out how obfuscation affects these detectors.</p> <p><code>RQ1_1_train.py</code> is to train four detectors with unobfuscated samples.</p> <p><code>RQ1_1_test.py</code> tests these trained detectors with unobfuscated and obfuscated samples.</p> <p><code>RQ1_2_train.py</code> is to train the detector <strong>ZOZZLE</strong> that uses different machine learning algorithms.</p> <p><code>RQ1_2_test.py</code> tests these trained models with unobfuscated and obfuscated samples.</p> <p><code>RQ1_3_train.py</code> uses a training set with all unobfuscated benign samples and all obfuscated malicious samples, and a training set with all obfuscated benign samples and all unobfuscated malicious samples to train the detectors.</p> <p><code>RQ1_3_test.py</code> uses these detectors to detect unobfuscated benign samples, obfuscated benign samples, unobfuscated malicious samples, and obfuscated malicious samples, respectively.</p> <p>RQ2</p> <p>The code under folder <code>RQ2/</code> is to study the two measures to mitigate the impact of obfuscation effective or not.</p> <p><code>RQ2_1_train.py</code> uses obfuscated samples to train four detectors.</p> <p><code>RQ2_1_test.py</code> tests these detectors on the same type of obfuscated samples.</p> <p><code>RQ2_2_test.py</code> tests thest detectors on the different type of obfuscated samples.</p> <p><code>RQ2_3.py</code> uses the BERT variants to generate code representation of unobfuscated samples, trains the detector with these code representations, and tests the trained detectors with code representations of obfuscated samples.</p> <p>RQ3</p> <p>The code unser fodler <code>RQ3/</code> visualizes the vectors, extracts the ten most important features, and calculates the distance between different sets of vectors.</p> <p>RQ4</p> <p>There is no code related to RQ4 here because the actual operation of RQ4 is to submit the samples to <a href="https://www.virustotal.com/gui/home/upload">VirusTotal</a> .</p> <p> </p> <p>The whole dataset is available at <a href="https://drive.google.com/file/d/1a7pNUwzikiJyY9L7dIu53I6_MR0oDpgi/view?usp=sharing." target="_blank" rel="noopener">https://drive.google.com/file/d/1a7pNUwzikiJyY9L7dIu53I6_MR0oDpgi/view?usp=sharing.</a></p> <p> </p> <p>Cite this work</p> <pre>@inproceedings{staticanalysis, author = {Kunlun Ren, Qiang Weizhong, Yueming Wu, Yi Zhou, Deqing Zou, Hai Jin}, title = {An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors}, booktitle = {Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'23)}, year = {2023} }</pre> <p> </p>
Obfuscation of function block diagrams
<p>Supplementary files to:</p> <p>A. Pakonen, "Obfuscation of function block diagrams," 28th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2023), September 12-15, 2023, Sinaia, Romania. IEEE, 2023.</p>
Phylogenomics of piranhas and pacus (Serrasalmidae) uncovers how dietary convergence and parallelism obfuscate traditional morphological taxonomy
Open the record for dataset details and reuse information.
Reproduction package for: Intelligent Match Merging to Prevent Obfuscation Attacks on Software Plagiarism Detectors
<p>This repository serves as the reproduction package for the master's thesis titled 'Intelligent Match Merging to Prevent Obfuscation Attacks on Software Plagiarism Detectors'. It includes datasets, experimental results and the implementation of the proposed approach. For additional details on the motivation, methodology, and analysis, please refer to the corresponding thesis document.</p>
PAN16 Author Obfuscation: Author-Masking
<p>We provide you with a training corpus that consists of documents that are to be obfuscated, and other documents from the same author.</p> <p>More information: <a href="https://pan.webis.de/clef16/pan16-web/author-masking.html">Link</a></p> <p>Browser: <a href="https://data.webis.de/masking">https://data.webis.de/masking</a></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.