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Microsoft Malware Classification Challenge

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arxiv 1802.10135 v1 pith:AYEVJYMV submitted 2018-02-22 cs.CR

Royi Ronen , Marian Radu , Corina Feuerstein , Elad Yom-Tov , Mansour Ahmadi This is my paper · ORCID
classification cs.CR
keywords datasetmalwareresearchchallengeclassificationcomparisonmicrosoftalong
verification ladder T0 review T1 audit T2 compute T3 formal
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The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 research papers. Here we provide a high-level comparison of the publications citing the dataset. The comparison simplifies finding potential research directions in this field and future performance evaluation of the dataset.

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Forward citations

Cited by 5 Pith papers

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

  1. HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection

    cs.CR 2026-07 conditional novelty 6.0 of 10

    Hilbert-curve plus class-relative entropy maps produce three-channel malware images that let shallow CNNs and few-shot models reach near-SOTA detection accuracy with lower inference cost.

  2. Signal-Based Malware Classification Using 1D CNNs

    cs.CR 2025-09 conditional novelty 5.0 of 10

    Resizing malware binaries to 1D signals and classifying them with 1D CNNs yields slight F1 improvements over 2D byteplot image models on MalNet.

  3. Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification

    cs.LG 2025-07 reject novelty 5.0 of 10

    A D3QN agent that jointly selects features and classifies malware reaches about 99% accuracy on two benchmarks, but the claimed efficiency gain fails because the full feature vector is always in the network input.

  4. FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning

    cs.CR 2025-07 reject novelty 4.0 of 10

    FedP3E shares noisy class prototypes across federated clients plus SMOTE augmentation, reporting 95.1 to 99.6% accuracy on N-BaIoT under non-IID splits, beating FedAvg and FedProx.

  5. Cross-Domain Malware Detection via Probability-Level Fusion of Lightweight Gradient Boosting Models

    cs.CR 2025-08 reject novelty 3.0 of 10

    A grid-search weighted average of LightGBM models on EMBER, API-call, and CIC-memory data is reported to reach 0.823 macro F1, but the validation set is both the tuning set and the test set, and the feature alignment ...

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