An ensemble approach for imbalanced multiclass malware classification using 1D-CNN.

ABSTRACT: Dependence on the internet and computer programs demonstrates the significance of computer programs in our day-to-day lives. Such demands motivate malware developers to create more malware, both in terms of quantity and variety. Researchers are constantly faced with hurdles while attempting to protect themselves from potential hazards and risks due to malware authors' usage of code obfuscation techniques. Metamorphic and polymorphic variations are easily able to elude the widely utilized signature-based detection procedures. Researchers are more interested in deep learning approaches than machine learning techniques to analyze the behavior of such a vast number of virus variants. Researchers have been drawn to the categorization of malware within itself in addition to the classification of

SUBMITTER: Panda B 

PROVIDER: S-EPMC10702937 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature