Hybrid feature fusion of API calls and n-grams with voting-based classifier fusion achieves 99.72% accuracy and 0.989 AUC for malware family classification on Microsoft dataset.
Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, , year=
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A Hybrid Approach For Malware Classification Using Secondary Features Fusion
Hybrid feature fusion of API calls and n-grams with voting-based classifier fusion achieves 99.72% accuracy and 0.989 AUC for malware family classification on Microsoft dataset.