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Applying Wide & Deep Learning Model for Android Malware Classification

Computer Systems Science & Engineering 2023年第6期45卷 2741-2759页

作者： Le Duc Thuan Pham Van Huong Hoang Van Hiep Nguyen Kim Khanh Ha Noi University of Science and Technology Ha Noi100000Viet Nam Academy of Cryptography Techniques Ha Noi100000Viet Nam

Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile *** the advancement of deep learning technology,numerous deep learning-based works have been proposed for the classification of Android *** learning technology is designed to handle a large amount of raw and continuous data,such as image content ***,it is incompatible with discrete features,i.e.,features gathered from multiple ***,if the feature set is already well-extracted and sparsely distributed,this technology is less effective than traditional machine *** the other hand,a wide learning model can expand the feature set to enhance the classification *** maximize the benefits of both methods,this study proposes combining the components of deep learning based on multi-branch CNNs(Convolutional Network Neural)with wide learning *** feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep *** proposed model,partition,and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features:API,permission,and raw *** accuracy with Wide and Deep CNN(WDCNN)model is 98.64%,improved by 1.38%compared to RNN(Recurrent Neural Network)model.

关键词： Wide and deep(W&D)learning convolutional neural network image feature raw features generalized features

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