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ANDROID MALWARE CLASSIFICATION USING DEEP LEARNING

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[PE2025100] ANDROID MALWARE CLASSIFICATION USING DEEP LEARNING -33%

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In the present day, there is a growing inclination towards the adoption of digital transformation and artificial intelligence in smart device applications across diverse operating systems. This trend aligns with the advancements of the fourth industrial revolution and is being observed in numerous domains of social and economic activity

Danh mục Đồ Án - Luận Văn

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Ngày tải lên 21/03/2025

File download hotrodoan.vn_Android malware classification using deep learning.pdf

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Nguyễn Kim Cường

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11/03/2025

Survey on research related to detecting and classifying malware on Android from the inception of the Android operating system until now (especially studies from 2019 to the present). From these studies, I now have a more general view of the research directions in the problem of detecting and classifying malware on Android.

Regarding feature augmentation, I applied algorithms to augmentation features (Comatrix and Apriori), creating connections between features.

Regarding feature selection, I proposed a new method for the feature selection problem (based on popularity and contrast calculation). Regarding machine learning models and deep learning models, in the dissertation, I have applied traditional machine learning models such as SVM, KNN, and RF. Deep learning models such as DBN, CNN, RNN, etc. These models give positive experimental results. In addition to applying these existing models, I used the WDCNN model, which is improved from the CNN model. Experimentally, this model has many advantages and gives higher results than other deep learning models.

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