An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications
Fecha
2021
Autores
Anand, A.
Rani, S.
Anand, D.
Aljahdali, H. M.
Kerr, D.
Profesor Guía
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Articulo
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Editor
Sensors (Basel)
Ubicación
https://doi.org/10.3390/s21196346
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1424-8220
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Departamento o Escuela
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Nota general
Resumen
The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier-Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 � 32 � 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.
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Palabras clave
DEEP LEARNING, DELIVERY OF HEALTH CARE HUMANS NEURAL NETWORKS, COMPUTER 5G-IOT CNN DEEP LEARNING HEALTHCARE MALIMG MALWARE