# EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

arXiv: https://arxiv.org/abs/1804.04637 · HTML: https://mlsanity.com/paper/1804.04637/

## Key facts

- Cited by 165 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 30 of 165)

- [Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks](https://mlsanity.com/paper/2110.06904/) (2021)
- [Towards A Proactive ML Approach for Detecting Backdoor Poison Samples](https://mlsanity.com/paper/2205.13616/) (2022)
- [Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten](https://mlsanity.com/paper/2202.13585/) (2022)
- [BagFlip: A Certified Defense against Data Poisoning](https://mlsanity.com/paper/2205.13634/) (2022)
- [EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers](https://mlsanity.com/paper/2506.05074/) (2025)
- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [Adversarial Robustness with Non-uniform Perturbations](https://mlsanity.com/paper/2102.12002/) (2021)
- [MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels](https://mlsanity.com/paper/2111.15031/) (2021)
- [A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels](https://mlsanity.com/paper/2109.11126/) (2021)
- [Out of Distribution Data Detection Using Dropout Bayesian Neural Networks](https://mlsanity.com/paper/2202.08985/) (2022)
- [SoK: The Impact of Unlabelled Data in Cyberthreat Detection](https://mlsanity.com/paper/2205.08944/) (2022)
- [CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario](https://mlsanity.com/paper/2305.01236/) (2023)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers](https://mlsanity.com/paper/2202.05470/) (2022)
- [Recasting Self-Attention with Holographic Reduced Representations](https://mlsanity.com/paper/2305.19534/) (2023)
- [Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance](https://mlsanity.com/paper/2307.14657/) (2023)
- [On the Limitations of Continual Learning for Malware Classification](https://mlsanity.com/paper/2208.06568/) (2022)
- [FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis](https://mlsanity.com/paper/2308.05362/) (2023)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints](https://mlsanity.com/paper/2108.04081/) (2021)
- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [XRand: Differentially Private Defense against Explanation-Guided Attacks](https://mlsanity.com/paper/2212.04454/) (2022)
- [MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition](https://mlsanity.com/paper/2305.01245/) (2023)
- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations](https://mlsanity.com/paper/2208.12248/) (2022)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)](https://mlsanity.com/paper/2402.01359/) (2024)
- [Adversarial Transfer Attacks With Unknown Data and Class Overlap](https://mlsanity.com/paper/2109.11125/) (2021)
- [MORPH: Towards Automated Concept Drift Adaptation for Malware Detection](https://mlsanity.com/paper/2401.12790/) (2024)
- [MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification](https://mlsanity.com/paper/2501.01110/) (2025)
- [MalPhase: Fine-Grained Malware Detection Using Network Flow Data](https://mlsanity.com/paper/2106.00541/) (2021)

## Abstract (arXiv)

This paper describes EMBER: a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files: 900K training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test samples (100K malicious, 100K benign). To accompany the dataset, we also release open source code for extracting features from additional binaries so that additional sample features can be appended to the dataset. This dataset fills a void in the information security machine learning community: a benign/malicious dataset that is large, open and general enough to cover several interesting use cases. We enumerate several use cases that we considered when structuring the dataset. Additionally, we demonstrate one use case wherein we compare a baseline gradient boosted decision tree model trained using LightGBM with default settings to MalConv, a recently published end-to-end (featureless) deep learning model for malware detection. Results show that even without hyper-parameter optimization, the baseline EMBER model outperforms MalConv. The authors hope that the dataset, code and baseline model provided by EMBER will help invigorate machine learning research for malware detection, in much the same way that benchmark datasets have advanced computer vision research.