Introduces L_ht-SVM using a hybrid truncated loss for robust sparse single-view classification with an ADMM solver, plus a multi-view extension MvL_ht-SVM.
Neurocomputing 623, 129406 (2025)
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MalGEN generates 977 executable malware samples across 1920 settings, with 45.71% evading existing detection engines and exposing gaps in current defenses.
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Robust and sparse support vector machine via hybrid truncated loss for supervised classification
Introduces L_ht-SVM using a hybrid truncated loss for robust sparse single-view classification with an ADMM solver, plus a multi-view extension MvL_ht-SVM.
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MalGEN: A Testbed for Modeling and Evaluating Malware Behaviors
MalGEN generates 977 executable malware samples across 1920 settings, with 45.71% evading existing detection engines and exposing gaps in current defenses.