Journal
SENSORS
Volume 21, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/s21196346
Keywords
healthcare; 5G-IoT; deep learning; malware; CNN; malimg
Ask authors/readers for more resources
5G-IoT plays an indispensable role in smart applications and is crucial in e-health applications. Intelligent schemes and architectures are required to overcome security threats against sensitive patient data in e-health applications. Deep learning techniques can detect security attacks, but this requires hybrid models for support.
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 x 32 x 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available