mlsanityData to 7 Oct 2026

Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features

arXiv 1508.03096 ↗First posted 13 Aug 2015cs.CRcited by 48

Not on the map. Why?

Abstract (arXiv)

Malware remains a serious problem for corporations, government agencies, and individuals, as attackers continue to use it as a tool to effect frequent and costly network intrusions. Machine learning holds the promise of automating the work required to detect newly discovered malware families, and could potentially learn generalizations about malware and benign software that support the detection of entirely new, unknown malware families. Unfortunately, few proposed machine learning based malware detection methods have achieved the low false positive rates required to deliver deployable detectors. In this paper we a deep neural network malware classifier that achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware. Specifically, we show that our system achieves a 95% detection rate at 0.1% false positive rate (FPR), based on more than 400,000 software binaries sourced directly from our customers and internal malware databases. We achieve these results by directly learning on all binaries, without any filtering, unpacking, or manually separating binary files into categories. Further, we confirm our false positive rates directly on a live stream of files coming in from Invincea's deployed endpoint solution, provide an estimate of how many new binary files we expected to see a day on an enterprise network, and describe how that relates to the false positive rate and translates into an intuitive threat score. Our results demonstrate that it is now feasible to quickly train and deploy a low resource, highly accurate machine learning classification model, with false positive rates that approach traditional labor intensive signature based methods, while also detecting previously unseen malware.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 11; 2023: 7; 2024: 5; 2025: 6; 2026: 6.

48 papers.

Sort:

By area:

  1. 2022Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgottencited by 25
  2. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  3. 2025EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifierscited by 18
  4. 2023Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performancecited by 12
  5. 2021Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraintscited by 11
  6. 2022Self-Supervised Vision Transformers for Malware Detectioncited by 5
  7. 2023AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corporacited by 5
  8. 2023Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothingcited by 5
  9. 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systemscited by 5
  10. 2021Artificial Neural Network for Cybersecurity: A Comprehensive Reviewcited by 4
  11. 2024SLIFER: Investigating Performance and Robustness of Malware Detection Pipelinescited by 4
  12. 2021Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weightscited by 3
36 more
  1. 2024MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learningcited by 3
  2. 2022Machine Learning for Detecting Malware in PE Filescited by 2
  3. 2023Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based Testingcited by 2
  4. 2025Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corporacited by 2
  5. 2026Cybersecurity is the True Frontier for Generative AI Success or Failurecited by 2
  6. 2021Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarmscited by 1
  7. 2021A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusioncited by 1
  8. 2021Segmented Federated Learning for Adaptive Intrusion Detection Systemcited by 1
  9. 2021Power-Based Attacks on Spatial DNN Acceleratorscited by 1
  10. 2021LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approachcited by 1
  11. 2021AI Total: Analyzing Security ML Models with Imperfect Data in Productioncited by 1
  12. 2022Toward the Detection of Polyglot Filescited by 1
  13. 2022SeqNet: An Efficient Neural Network for Automatic Malware Detectioncited by 1
  14. 2022On deceiving malware classification with section injectioncited by 1
  15. 2023Interpreting GNN-based IDS Detections Using Provenance Graph Structural Featurescited by 1
  16. 2023A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attackscited by 1
  17. 2024Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Researchcited by 1
  18. 2025Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detectioncited by 1
  19. 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Modelscited by 1
  20. 2021Anomaly Detection Support Using Process Classificationcited by 0
  21. 2021Defending Against Adversarial Denial-of-Service Data Poisoning Attackscited by 0
  22. 2021A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binariescited by 0
  23. 2022RoboMal: Malware Detection for Robot Network Systemscited by 0
  24. 2022A Natural Language Processing Approach for Instruction Set Architecture Identificationcited by 0
  25. 2022Firenze: Model Evaluation Using Weak Signalscited by 0
  26. 2022Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detectioncited by 0
  27. 2022Adversarial Attacks on Transformers-Based Malware Detectorscited by 0
  28. 2023Sequential Embedding-based Attentive (SEA) classifier for malware classificationcited by 0
  29. 2024EGAN: Evolutional GAN for Ransomware Evasioncited by 0
  30. 2025Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAGcited by 0
  31. 2025Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systemscited by 0
  32. 2025Phishing Detection System: An Ensemble Approach Using Character-Level CNN and Feature Engineeringcited by 0
  33. 2026Machine Learning Transferability for Malware Detectioncited by 0
  34. 2026Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEEcited by 0
  35. 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usabilitycited by 0
  36. 2026Delphi Scanner: efficient and interpretable static malware detection via API sequence modelingcited by 0