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• W4313445185 abstract "Due to the rise in smartphone apps and increase in the usage of Android, there are a lot of security issues. Security issues need to be addressed in order to prevent vulnerabilities and identify them prior mishap. People who use smartphones are linked to a warning about the risks. Most people who use mobile phones do not have to think about a few negative possibilities when they install Android Package Kit (APK) files from different sources. It is important to have a system that can tell if the code in Android app is harmful. The first step in this work is to look at the Android APK datasets. Both good and bad APKs are analyzed, and the dataset is processed. The signatures that are hidden in the APKs are identified and extracted. This will make it easier to build a training dataset. Then, it is checked to see if each APK has the permissions it needs and how it affects the way it works. Once it’s been cleaned, a dataset is made so that the model can be trained to predict malware. To finish the predictive analytics, any APK outside of the APKs is chosen and used. That’s when it is possible to figure out how likely it is that the new APK will have corruptive code in it. Machine learning is used to track the results of different prediction measures, such as how long it takes, how accurate they are, and how much they cost. This article describes a machine learning technique to solve functional selection by safeguarding the selection and mutation operators of genetic algorithms. The proposed method is adaptable during population calculations in the training set. Furthermore, for various population sizes, it gives the best possible probability of resolving function selection difficulties during the training process. Furthermore, the 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, wherein this approach shows elevated results in terms of accuracy." @default.
• W4313445185 created "2023-01-06" @default.
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• W4313445185 date "2023-01-01" @default.
• W4313445185 modified "2023-10-16" @default.
• W4313445185 title "An Effectual Analytics and Approach for Avoidance of Malware in Android Using Deep Neural Networks" @default.
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• W4313445185 doi "https://doi.org/10.1007/978-981-19-5443-6_58" @default.
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