Abstract. The paper proposes an Android malware detection method based on convolutional neural network mixed-data model. This data are presented by API.
Feb 23, 2024 · This study introduces an innovative deep convolutional neural network (D-CNN) method that cleverly integrates permission features and API call graphs.
Feb 15, 2021 · We come to the point of designing a convolution neural network to analyze the images and predict the maliciousness of a file.
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We propose a system that combines AI-based malware detection and classification systems trained on both static and dynamic features.
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This project focuses on leveraging the power of CNNs, a deep learning technique commonly used in computer vision tasks, to classify malware samples into ...
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This paper proposes to detect malwares according to very small binary fragments of PE files by using a CNN-based model.
Our paper offers convolutional neural network-based malware detection method that is very accurate and efficient. The system proceeds with binary file as ...
Our article offers convolutional neural network-based malware detection method that is very accurate and efficient. The system proceeds with binary file as ...
Dec 15, 2024 · So, this paper brings an effective DL based detection of malware in which the following are the stages: a) Data collection being carried from ...
This paper aims to assess the efficacy of the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, emphasising its significance in tackling ...