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

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Published: Aug 18, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i8s.7235
Keywords:
Genetic Approaches, JAYA algorithm, Feature Selection, Accuracy, Dimensionality Reduction

Main Article Content

D. Sandhya Rani
Professor, Department of CSE, CVR College of Engineering, Ibrahimpatnam, Rangareddy, India
G Bala Krishna
Professor, Department of CSE, CVR College of Engineering, Ibrahimpatnam, Rangareddy, India
M Radha
Associate Professor, Department of CSE, CVR College of Engineering, Ibrahimpatnam, Rangareddy, India
Sirisha K L S
Assistant Professor, Department of CSE, Keshav Memorial Institute of Technology, Narayanguda, Hyderabad, India
V Pradeep Kumar
Associate Professor, Department of CSE, B V Raju Institute of Technology, Narsapur, Medak, India
Meduri Anupama
Associate Professor, Department of CSE, Maturi Venkata Subba Rao(MVSR) Engineering College, Hyderabad, India

Abstract

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

Article Details

How to Cite
Rani, D. S. ., Krishna, G. B. ., Radha, M. ., K L S, S. ., Kumar, V. P. ., & Anupama, M. . (2023). GBJOF: Gradient Boosting Integrated with Jaya Algorithm to Optimize the Features in Malware Analysis . International Journal on Recent and Innovation Trends in Computing and Communication, 11(8s), 538–548. https://doi.org/10.17762/ijritcc.v11i8s.7235
Issue
Vol. 11 No. 8s (2023)
Section
Articles

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