Papers › Deep learning at the shallow end: Malware classification for non-domain experts
Deep learning at the shallow end: Malware classification for non-domain experts
Quan Le, Oisín Boydell, Brian Mac Namee, Mark Scanlon
Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.
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 | CNN BiLSTM - Reb Sampl | Accuracy (5-fold) | 98.20 | #26 of 29 | Archive leaderboard | report |
| Malware Classification | Microsoft Malware Classification Challenge | CNN BiLSTM - Reb Sampl | F1 score (5-fold) | 96.05 | #26 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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