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Malware Detection by Eating a Whole EXE

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arxiv 1710.09435 v1 pith:EIKXPEES submitted 2017-10-25 stat.ML cs.CRcs.LG

Edward Raff , Jon Barker , Jared Sylvester , Robert Brandon , Bryan Catanzaro , Charles Nicholas This is my paper
classification stat.MLcs.CRcs.LG
keywords problembuildingdetectionworkchallengeslearningmalwarenetwork
verification ladder T0 review T1 audit T2 compute T3 formal
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In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that detection from raw bytes presents a sequence problem with over two million time steps and a problem where batch normalization appear to hinder the learning process. We present our initial work in building a solution to tackle this problem, which has linear complexity dependence on the sequence length, and allows for interpretable sub-regions of the binary to be identified. In doing so we will discuss the many challenges in building a neural network to process data at this scale, and the methods we used to work around them.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Large Byte Model: Teaching Language Models About Compiled Code

    cs.CR 2026-06 unverdicted novelty 7.0 of 10

    Presents a byte-native LLM with bespoke tokenizer achieving 69-98% accuracy on malware family and architecture classification from raw bytes.

  2. Latent Stability Analysis of Malware Representations Under Feature-Space Perturbations

    cs.CR 2026-07 conditional novelty 5.5 of 10

    Latent-stability metrics (LED and PINNFlow shifts) diagnose malware representation movement under feature-space probes, without beating full EMBER or PCA-64 on clean classification.

  3. Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation

    cs.CR 2026-05 unverdicted novelty 5.0 of 10

    The paper releases two adversarial malware datasets (44k family-labelled, 33k type-labelled) with high evasion rates and demonstrates that 0.5% poisoning injection raises evasion from 26.1% to 92.8%.

  4. Cybersecurity is the True Frontier for Generative AI Success or Failure

    cs.CR 2026-06 unverdicted novelty 3.0 of 10

    Cybersecurity's scale, adversaries, labeling issues, and operational demands make it the superior test-case for general AI progress over NLP or computer vision.

  5. Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset

    cs.CR 2025-07 conditional novelty 3.0 of 10

    On the EMBER malware dataset, LightGBM and XGBoost achieve the highest detection accuracy, while PCA and LDA help KNN but degrade boosting models.

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