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This article discusses the development of a new malware classification system that utilizes multi-objective genetic algorithms (MOGAs) for feature selection, enhancing the detection of evolving malware threats. The study refines the feature set from 335 to 200, achieving a peak accuracy of 93.33% and a 40% performance improvement in malware detection. By employing machine learning algorithms within a hybrid framework, the research demonstrates significant advancements in the effectiveness of malware analysi
- Author
- 20521963
- Language
- EN