An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

Ankita Anand; Shalli Rani; Divya Anand; Hani Moaiteq Aljahdali; Dermot Kerr

doi:10.3390/s21196346

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

Sensors (Basel). 2021 Sep 23;21(19):6346. doi: 10.3390/s21196346.

Authors

Ankita Anand¹, Shalli Rani¹, Divya Anand², Hani Moaiteq Aljahdali³, Dermot Kerr⁴

Affiliations

¹ Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India.
² Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India.
³ Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 37848, Saudi Arabia.
⁴ Intelligent Systems Research Centre, University of Ulster, Londonderry BT48 7JL, UK.

Abstract

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.

Keywords: 5G-IoT; CNN; deep learning; healthcare; malimg; malware.

MeSH terms

Deep Learning*
Delivery of Health Care
Humans
Neural Networks, Computer