An efficient CNN-based deep learning model to detect malware attacks (CNN-DMA) in 5G-IoT healthcare applications
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 …
part in e-health applications. E-health applications require intelligent schemes and …
A deep learning system for health care IoT and smartphone malware detection
The use of smart and connected devices, such as Android and Internet of Things (IoT) have
increased exponentially. In the last 10 years, mobiles and IoT devices have surpassed PC's …
increased exponentially. In the last 10 years, mobiles and IoT devices have surpassed PC's …
A Multi-View attention-based deep learning framework for malware detection in smart healthcare systems
V Ravi, M Alazab, S Selvaganapathy… - Computer …, 2022 - Elsevier
Recent security attack reports show that the number of malware attacks is gradually growing
over the years due to the rapid adoption of smart healthcare systems. The development of a …
over the years due to the rapid adoption of smart healthcare systems. The development of a …
Deep learning methods for malware and intrusion detection: A systematic literature review
Android and Windows are the predominant operating systems used in mobile environment
and personal computers and it is expected that their use will rise during the next decade …
and personal computers and it is expected that their use will rise during the next decade …
Malware detection in internet of things (IoT) devices using deep learning
Internet of Things (IoT) devices usage is increasing exponentially with the spread of the
internet. With the increasing capacity of data on IoT devices, these devices are becoming …
internet. With the increasing capacity of data on IoT devices, these devices are becoming …
An efficient combined deep neural network based malware detection framework in 5G environment
While Android smartphones are widely used in 5G networks, third-party application
platforms are facing a rapid increase in the screening of applications for market launch …
platforms are facing a rapid increase in the screening of applications for market launch …
IIoT malware detection using edge computing and deep learning for cybersecurity in smart factories
H Kim, K Lee - Applied Sciences, 2022 - mdpi.com
The smart factory environment has been transformed into an Industrial Internet of Things
(IIoT) environment, which is an interconnected and open approach. This has made smart …
(IIoT) environment, which is an interconnected and open approach. This has made smart …
A comprehensive survey on deep learning based malware detection techniques
M Gopinath, SC Sethuraman - Computer Science Review, 2023 - Elsevier
Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …
harmful threats to the digital world. Malware mitigation techniques have evolved over the …
MCFT-CNN: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in Internet of Things
S Kumar - Future Generation Computer Systems, 2021 - Elsevier
With ever-increasing, internet-connected devices provide an opportunity to fulfil the
attacker's malicious intention. They use malicious programs to compromise the devices and …
attacker's malicious intention. They use malicious programs to compromise the devices and …
A novel detection and multi-classification approach for IoT-malware using random forest voting of fine-tuning convolutional neural networks
The Internet of Things (IoT) is prone to malware assaults due to its simple installation and
autonomous operating qualities. IoT devices have become the most tempting targets of …
autonomous operating qualities. IoT devices have become the most tempting targets of …