Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 3 — Issue 2 — June 2021

Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division

https://doi.org/10.36548/jucct.2021.2.006
B Vivekanandam
Senior Lecturer, Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia
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Vivekanandam, B. 2021. “Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division”. Journal of Ubiquitous Computing and Communication Technologies 3 (2): 135-49. https://doi.org/10.36548/jucct.2021.2.006.
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Keywords

— Genetic algorithm
— android malware detection process
Published: 23-07-2021

Abstract

Data pre-processing is critical for handling classification issues in the field of machine learning and model identification. The processing of big data sets increases the computer processing time and space complexity while decreasing classification model precision. As a result, it is necessary to develop an appropriate method for selecting attributes. This article describes a machine learning technique to solve functional selection by safeguarding the selection and mutation operators of genetic algorithms. During population calculations in the training set, the proposed method is adaptable. Furthermore, for various population sizes, the proposed method gives the best possible probability of resolving function selection difficulties during training process. Furthermore, the proposed work is combined with a better classifier in order to detect the different malware categories. The proposed approach is compared and validated with current techniques by using different datasets. In addition to the test results, this research work utilizes the algorithm for solving a real challenge in Android categorization, and the results show that, the proposed approach is superior. Besides, the proposed algorithm provides a better mean and standard deviation value in the optimization process for leveraging model effectiveness at different datasets.

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