mlsanityData to 14 Oct 2026

Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

arXiv 2012.09390 ↗First posted 17 Dec 2020stat.ML cs.AI cs.LGcited by 24

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Abstract (arXiv)

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed 100 MB, which corresponds to a time series with T=100,000,000 steps. To date, the closest approach to handling such a task is MalConv, a convolutional neural network capable of processing up to T=2,000,000 steps. The O(T) memory of CNNs has prevented further application of CNNs to malware. In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length T. This makes MalConv 116× more memory efficient, and up to 25.8× faster to train on its original dataset, while removing the input length restrictions to MalConv. We re-invest these gains into improving the MalConv architecture by developing a new Global Channel Gating design, giving us an attention mechanism capable of learning feature interactions across 100 million time steps in an efficient manner, a capability lacked by the original MalConv CNN. Our implementation can be found at https://github.com/NeuromorphicComputationResearchProgram/MalConv2

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 7; 2025: 1; 2026: 7.

24 papers.

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  1. 2021MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labelscited by 17
  2. 2023RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletioncited by 17
  3. 2023Recasting Self-Attention with Holographic Reduced Representationscited by 14
  4. 2025MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classificationcited by 8
  5. 2024Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detectioncited by 6
  6. 2024Is Function Similarity Over-Engineered? Building a Benchmarkcited by 6
  7. 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systemscited by 5
  8. 2023Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!cited by 4
  9. 2021Evading Malware Classifiers via Monte Carlo Mutant Feature Discoverycited by 2
  10. 2026Cybersecurity is the True Frontier for Generative AI Success or Failurecited by 2
  11. 2022Toward the Detection of Polyglot Filescited by 1
  12. 2022SeqNet: An Efficient Neural Network for Automatic Malware Detectioncited by 1
12 more
  1. 2024Explainable Malware Detection with Tailored Logic Explained Networkscited by 1
  2. 2024A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfercited by 1
  3. 2021A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binariescited by 0
  4. 2023FGAM:Fast Adversarial Malware Generation Method Based on Gradient Signcited by 0
  5. 2024Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothingcited by 0
  6. 2024A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognitioncited by 0
  7. 2026ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coveragecited by 0
  8. 2026Large Byte Model: Teaching Language Models About Compiled Codecited by 0
  9. 2026SoK: AI-Augmented Binary Reversingcited by 0
  10. 2026Delphi Scanner: efficient and interpretable static malware detection via API sequence modelingcited by 0
  11. 2026Fast And Accurate Text Content File Type Identificationcited by 0
  12. 2026Bytes to Threats: Byte-Sequence Transformer For Malware Detectioncited by 0