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

B. Vivekanandam
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引用次数: 22

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.
基于遗传算法的自适应混合检测方法在Android Division中实现有效的恶意软件检测
在机器学习和模型识别领域,数据预处理是处理分类问题的关键。大数据集的处理增加了计算机处理的时间和空间复杂度,降低了分类模型的精度。因此,有必要开发一种合适的方法来选择属性。本文介绍了一种机器学习技术,通过保护遗传算法的选择算子和变异算子来解决功能选择问题。在训练集的总体计算过程中,该方法具有较强的适应性。此外,对于不同的人口规模,该方法给出了解决训练过程中函数选择困难的最佳概率。此外,所提出的工作与更好的分类器相结合,以检测不同的恶意软件类别。通过使用不同的数据集,将所提出的方法与现有技术进行了比较和验证。除了测试结果外,本研究工作还将该算法用于解决Android分类中的一个实际挑战,结果表明,本文提出的方法具有优越性。此外,该算法在优化过程中提供了更好的均值和标准差值,以充分利用模型在不同数据集上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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