GBJOF: Gradient Boosting Integrated with Jaya Algorithm to Optimize the Features in Malware Analysis

Malware analysis is used to identify suspicious file transferring in the network. It can be identified efficiently by using reverse engineering hybrid approach. Implementing a approach depends on feature selection because dataset contains static and dynamic parameters. The given 85 attributes with 10 different class labels. Since it has high dimensional multi-classification data, existing approaches of ML could more efficient reducing features. model combines enhanced JAYA genetic algorithm gradient boosting technique efficiency smaller number Many for either implement correlation or wrapper techniques. major disadvantages these issues are that they facing fitting problems very small With Usage approach, this paper achieved 95% accuracy 12 features, approximately 7% greater than approaches.