Papers › Sequential Embedding-based Attentive (SEA) classifier for malware classification

Sequential Embedding-based Attentive (SEA) classifier for malware classification

11 Feb 2023arXiv:2302.05728archive 2025-07-28

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.

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Tasks

ClassificationMalware ClassificationMalware Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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