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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteFor a fast, reproducible Windows PE baseline, start with EMBER. Choose SOREL-20M when you can support roughly 8 TB of storage and need tens of millions of labeled examples, BODMAS when family labels and temporal analysis are central, Drebin for Android, MalwareBazaar for fresh samples and external testing, and EMBER2024 for multi-format tasks.
The important distinction is what each project actually distributes: precomputed features, original or disarmed binaries, metadata, family labels, or a changing sample feed. Those differences determine which experiments are feasible, how you split data, and whether another team can reproduce your result.
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Dataset comparison at a glance
| Dataset | Platform | Scale and labels | Data form | Best use | Main limitation |
|---|---|---|---|---|---|
| EMBER | Windows PE | Versioned benign/malicious feature releases; exact sample totals vary by release | Precomputed features and feature-extraction code | Fast, reproducible static baselines | Older collection windows and different 2017/2018 selection criteria |
| SOREL-20M | Windows PE | Approximately 10 million benign and 10 million malicious files | EMBER-v2 features, metadata, labels, telemetry counts, behavioral tags, and approximately 10 million disarmed binaries | Large-scale training and temporal experiments | The complete collection is approximately 8 TB; terms of use apply |
| BODMAS | Windows PE | 134,435 samples from 581 families: 57,293 malware and 77,142 benign | Hashes and PE samples with family and temporal context | Family classification and concept-drift studies | Historical collection from August 2019 through September 2020 |
| Drebin | Android | 5,560 applications from 179 malware families | Android applications distributed through controlled access | Android static malware classification | Access review is required; applications were collected from August 2010 through October 2012 |
| MalwareBazaar | Multiple malware formats | More than 1,155,941 confirmed samples reported in its FAQ | Current sample-sharing platform and metadata | Freshness, corpus refreshes, and external evaluation | Counts, availability, and procedures change over time |
| EMBER2024 | Six file formats | More than 3.2 million files and seven classification tasks | Hashes, metadata, features, and labels | Multi-format and broader evaluation | Documentation is preprint-level; verify release status and terms |
How to choose a malware dataset
Match the platform to the deployment question
Windows PE datasets are appropriate for Windows executables, not for Android packages, scripts, documents, or network traffic. EMBER, SOREL-20M, and BODMAS all target PE classification. Drebin targets Android applications. MalwareBazaar spans multiple malware formats, while EMBER2024 is explicitly designed around six formats. A model trained on PE headers and sections cannot be treated as an Android detector simply because both tasks use the words “malware” and “benign.”
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Feature releases are usually the quickest path to a defensible baseline. EMBER supplies versioned feature archives and extraction code, so a researcher can reproduce a static model without downloading and safely handling a very large binary corpus. SOREL-20M adds EMBER-v2 features to metadata and disarmed binaries. BODMAS provides hashes and PE samples with family and temporal context. Drebin distributes Android applications through a controlled process. A feed such as MalwareBazaar is useful when the experiment requires newly collected samples, but it is less suitable as the sole fixed benchmark because its contents change.
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Choose label granularity before choosing an algorithm
Binary benign-versus-malicious classification needs different labels from family identification. EMBER is the straightforward baseline for the former. BODMAS is the clearest choice when the target is family classification, because its release reports 581 families. SOREL-20M includes labels, telemetry counts, and behavioral tags that support richer analyses than a single binary target. Drebin’s 179 families enable Android family experiments. For MalwareBazaar, inspect the metadata and define your own label policy; the platform’s sample count is not itself a guarantee of consistent family labels.
Budget storage, processing, and safe handling
Scale changes the engineering problem. The full SOREL-20M collection is approximately 8 TB, before you account for working space, indexes, decompressed files, model checkpoints, and backups. Feature-only workflows can be dramatically smaller. Any workflow involving executable files should use an isolated analysis environment, restrict network access, record cryptographic hashes, and separate raw samples from derived features. Do not assume that a public repository permits redistribution of binaries.
Plan the time split before training
Malware evolves, labels arrive late, and near-duplicate files can cross a random split. BODMAS was assembled with temporal analysis in mind, and EMBER documents different selection criteria for its 2017 and 2018 releases. For a realistic generalization estimate, keep earlier files for training and later files for validation or testing. If you use a random split for a quick baseline, also report a hash- and time-aware split so readers can see how much performance depends on leakage.
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EMBER: the practical reproducible baseline
What it contains
EMBER is designed for static Windows PE classification. Its public releases provide precomputed feature archives together with the code used to extract those features. That arrangement removes the largest operational burden in a first experiment: collecting, storing, and safely parsing every executable yourself. The normal task is benign-versus-malicious classification from a fixed feature representation.
Why the release year matters
EMBER documentation distinguishes 2017 and 2018 selections. The 2018 samples were selected so that “the resultant training and test sets would be harder for machine learning algorithms to classify.” Treat the release year as part of the dataset definition, not as an incidental filename. A result on one selection is not automatically comparable with a result on the other.
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When EMBER is the right choice
- You need a baseline that another team can download and process without a multi-terabyte binary archive.
- Your model consumes static PE features rather than raw bytes, disassembly, or dynamic traces.
- You want a stable benchmark for ablations, feature studies, or a first paper-quality implementation.
Limits to record in your paper
EMBER is not a current threat feed, and its release windows are historical. It also does not solve family attribution for you. Report the exact release, feature version, split, preprocessing, and any samples removed during cleaning. Verify the current download and usage terms before mirroring archives or publishing derived data.
SOREL-20M: scale for large Windows experiments
What the collection provides
SOREL-20M contains approximately 20 million PE files: roughly 10 million benign and 10 million malicious. Its materials include EMBER-v2 features, metadata, labels, telemetry counts, behavioral tags, and approximately 10 million disarmed binaries. The combination supports experiments that go beyond a single static score, such as studying telemetry-informed labels, behavioral subgroups, or models trained at unusual scale.
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The storage decision is part of the method
The project documentation states that the full dataset is approximately 8 TB. Plan for more than that in practice: temporary downloads, decompression, deduplication indexes, feature caches, and backups all consume space. If your question can be answered with feature archives, do not download binaries merely because they are available. Conversely, if your contribution depends on raw-file inspection or a new feature extractor, budget the storage and isolated processing infrastructure before committing to the dataset.
Good use cases
- Measuring how model capacity or sampling strategy changes with tens of millions of labeled examples.
- Training on static features while using metadata, telemetry counts, or behavioral tags for analysis.
- Testing temporal or distribution-shift methods where a small benchmark would be underpowered.
Operational and licensing cautions
SOREL-20M documentation points users to a public S3 bucket and separate terms of use. Public access is not the same as unrestricted redistribution. Read the current terms before sharing binaries, creating a mirror, or releasing a derived corpus that could enable reconstruction of the original files.
BODMAS: family labels and temporal evaluation
Published composition
The BODMAS paper reports 134,435 samples from 581 families: 57,293 malware samples and 77,142 benign files. It provides hashes and PE samples together with family and temporal context. Those labels make it more suitable than a purely binary benchmark when the research question is “which family is this?” rather than only “is this malicious?”
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Why its time window is useful
BODMAS covers August 2019 through September 2020. That historical window is a feature for concept-drift work: train on earlier material, test on later material, and measure how performance changes as malware families and benign software distributions move. It is not a substitute for a current feed, and a score from this period should not be described as present-day field performance.
Recommended split designs
- Deduplicate using cryptographic hashes before assigning data to a split.
- Construct a chronological split using the available collection dates.
- Keep family identities visible when evaluating family generalization; report whether test families were seen during training.
- Use a random split only as a secondary diagnostic, not as the sole estimate of deployment performance.
Best fit
Choose BODMAS when family coverage, temporal ordering, and a manageable PE corpus matter more than maximum sample count. It is also a useful complement to EMBER: one emphasizes a reproducible feature baseline, while the other gives you family and time structure for harder evaluations.
Drebin: a controlled Android benchmark
Scope and size
Drebin contains 5,560 Android applications from 179 malware families. The project description places collection between August 2010 and October 2012. Those dates are essential context: Android packaging, permissions, APIs, and malware behavior have changed substantially since then.
Access requirements
Drebin is not an anonymous, unrestricted download. The access process requires an email request with identity, affiliation, and research scope; an advisor or institutional address may be required. Allow time for review and keep the approved use aligned with the project’s conditions.
What it is good for
- Reproducing Android static-classification papers and feature-engineering baselines.
- Comparing family-aware methods on a known set of 179 families.
- Teaching or validating an Android analysis pipeline in a controlled, historically defined setting.
What it cannot establish
A high score on Drebin does not establish that a detector handles modern Android malware. Use the collection for reproducibility and method comparison, then seek a newer, separately governed evaluation source if recency is part of your claim.
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MalwareBazaar: freshness and external evaluation
How to treat the platform
MalwareBazaar is a changing sample-sharing platform rather than a frozen benchmark. Its FAQ reports more than 1,155,941 confirmed samples and describes multiple access methods. Counts and procedures can change, so record the retrieval date, query or feed parameters, sample hashes, metadata version, and every filtering rule.
Strong use cases
- Refreshing an older corpus with newer samples.
- Building a time-separated external test set after your training benchmark is fixed.
- Checking whether a model trained on EMBER, SOREL-20M, or BODMAS transfers to samples from a different collection process.
Why it should not be your only benchmark
A live feed changes underneath the experiment. Two teams can request data at different times and receive different populations, while family labels and metadata may not be uniform across all submissions. Keep a fixed benchmark for reproducibility and use MalwareBazaar as a documented supplement. Follow the platform’s current access, handling, and redistribution rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.EMBER2024: broader formats and tasks
What is new about the benchmark idea
The EMBER2024 preprint describes more than 3.2 million files spanning six formats and seven classification tasks. That moves beyond a single PE-only benign-versus-malicious benchmark and supports comparisons across file types and task definitions.
When to consider it
Use EMBER2024 when your contribution is multi-format, when you need tasks beyond binary classification, or when a holistic benchmark is more informative than a PE-only result. Read the preprint and release documentation carefully: the available materials, task partitions, and terms should be verified before you build a long-running experiment around them.
Reporting requirement
Name the exact formats and tasks used. “EMBER2024” alone is too broad to reproduce a result when the release contains seven classification tasks and six file formats.
A defensible workflow for any public malware dataset
1. Define the prediction target
Write down whether the target is binary detection, family classification, family novelty, malware type, or another task. Specify the unit of prediction: file, application, hash, or family. This prevents a family-labeled corpus from being reduced to a misleading binary score.
2. Freeze the dataset definition
Record the release name, retrieval date, file count, label policy, collection window, and source version. For changing platforms, save the exact hash list and metadata snapshot. For feature releases, record the feature schema and extractor version.
3. Verify and deduplicate
Use cryptographic hashes to identify duplicates before splitting. Keep a manifest containing hash, label, family when available, collection date, source, and processing status. Do not silently discard conflicts; flag duplicate hashes with different metadata and document the rule used to resolve them.
4. Separate raw samples from derived data
Store original or disarmed files in an isolated location and keep extracted features, labels, and indexes in a separate research workspace. Restrict execution, disable unnecessary network access, and preserve hashes so a feature row can be traced back to the exact input without redistributing the file.
5. Build a leakage-resistant split
Prefer chronological splits for drift claims and family-held-out splits for family generalization. Keep near-duplicates and variants from crossing train and test partitions when your goal is real-world novelty. A random split can still be useful for debugging, but label it as such.
6. Establish a simple baseline
Start with the dataset’s documented feature representation and a transparent classifier. Record preprocessing, missing-value handling, class weighting, threshold selection, and calibration. This baseline gives you a reference before adding deep models, raw-byte features, or auxiliary metadata.
7. Report more than accuracy
Include precision, recall, false-positive rate, area under the precision-recall curve, and confusion matrices where appropriate. For family tasks, report macro-averaged results so large families do not hide failures on small ones. State the operating threshold and the prevalence used for evaluation.
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After model selection, evaluate once on a separately collected, hash-documented set. A changing source such as MalwareBazaar is useful here, provided you publish the retrieval date, selection rules, and hashes or an approved summary that lets others understand what was tested.
Quick Recap
Common mistakes and how to avoid them
- Calling features “binaries.” EMBER’s normal distribution is precomputed features plus extraction code; do not claim that every original executable is available.
- Mixing collection eras without disclosure. EMBER’s 2017 and 2018 releases use different selection criteria, and Drebin is from 2010–2012.
- Using random splits for temporal claims. Sort by collection time and keep future material out of training.
- Ignoring class and family imbalance. Report the benign/malicious counts and family distribution used in your experiment, not just the headline dataset size.
- Treating a live feed as a fixed benchmark. Save hashes, metadata, retrieval dates, and filters whenever you use MalwareBazaar.
- Underestimating infrastructure. SOREL-20M’s approximately 8 TB full size excludes many working copies and indexes.
- Assuming public means freely redistributable. Check each project’s current terms before publishing samples, mirrors, or derived artifacts.
- Reporting one number across incomparable tasks. Distinguish PE, Android, and multi-format results and name the exact task and release.
Decision guide
| Your priority | First dataset to evaluate | Reason |
|---|---|---|
| Fast, reproducible Windows static baseline | EMBER | Versioned features and extraction code minimize data-engineering overhead. |
| Maximum labeled PE scale | SOREL-20M | Approximately 20 million files with features, metadata, and disarmed binaries. |
| Family attribution and temporal drift | BODMAS | 581 families and an explicit August 2019–September 2020 context. |
| Android malware classification | Drebin | 5,560 applications across 179 families with controlled distribution. |
| Fresh samples or an external test | MalwareBazaar | Continuously changing collection and multiple access methods. |
| Multi-format evaluation | EMBER2024 | More than 3.2 million files, six formats, and seven tasks described in its preprint. |
Access, attribution, and reproducibility checklist
- Read the current terms for the exact release you use.
- Record source, version, retrieval date, collection window, and label provenance.
- Store cryptographic hashes and a manifest for every included sample or feature row.
- Document whether the experiment used features, original binaries, disarmed binaries, or metadata only.
- Describe deduplication, family handling, temporal boundaries, and split construction.
- Do not publish binaries, mirrors, or reconstructed samples unless the governing terms explicitly allow it.
- For controlled-access collections, retain approval information and follow the requested research scope.
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