Papers › Convolutional Neural Network for Classification of Malware Assembly Code
Convolutional Neural Network for Classification of Malware Assembly Code
Daniel Gibert, Javier Béjar, Carles Mateu, Jordi Planes, Daniel Solis, Ramon Vicens
Traditional signature-based methods have started becoming inadequnate to deal with next generation malware which utilize sophisticated obfuscation (polymorphic and metamorphic) techniques to evade detection. Recently, research efforts have been conducted on malware detection and classification by applying machine learning techniques. Despite them, most methods are build on shallow learning architectures and rely on the extraction of hand-crafted features. In this paper, based on assembly language code extracted from disassembled binary files and embedded into vectors, we present a convolutional neural network architecture to learn a set of discriminative patterns able to cluster malware files amongst families. To demonstrate the suitability of our approach we evaluated our model on the data provided by Microsoft for the BigData Innovators Gathering 2015 Anti-Malware Prediction Challenge. Experiments show that the method achieves competitive results without relying on the manual extraction of features and is resilient to the most common obfuscation techniques.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Malware Classification | Microsoft Malware Classification Challenge | Opcode-based Shallow CNN | Accuracy (10-fold) | 0.9917 | #5 of 29 | Archive leaderboard | report |
| Malware Classification | Microsoft Malware Classification Challenge | Opcode-based Shallow CNN | LogLoss | 0.0244 | #5 of 29 | Archive leaderboard | report |
| Malware Classification | Microsoft Malware Classification Challenge | Opcode-based Shallow CNN | Macro F1 (10-fold) | 0.9856 | #5 of 29 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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