Papers › Sequential Embedding-based Attentive (SEA) classifier for malware classification
Sequential Embedding-based Attentive (SEA) classifier for malware classification
Muhammad Ahmed, Anam Qureshi, Jawwad Ahmed Shamsi, Murk Marvi
The tremendous growth in smart devices has uplifted several security threats. One of the most prominent threats is malicious software also known as malware. Malware has the capability of corrupting a device and collapsing an entire network. Therefore, its early detection and mitigation are extremely important to avoid catastrophic effects. In this work, we came up with a solution for malware detection using state-of-the-art natural language processing (NLP) techniques. Our main focus is to provide a lightweight yet effective classifier for malware detection which can be used for heterogeneous devices, be it a resource constraint device or a resourceful machine. Our proposed model is tested on the benchmark data set with an accuracy and log loss score of 99.13 percent and 0.04 respectively.
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 | SEA | Accuracy (10-fold) | 0.9912 | #7 of 29 | Archive leaderboard | report |
| Malware Classification | Microsoft Malware Classification Challenge | SEA | LogLoss | 0.0431 | #7 of 29 | Archive leaderboard | report |
| Malware Classification | Microsoft Malware Classification Challenge | SEA | Macro F1 (10-fold) | 0.9908 | #7 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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