ArticleSensors (Basel, Switzerland)2021
An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- AI-Driven Security: Detecting Cyber Attacks in IoT Networks.Sensors (Basel, Switzerland) · 2026Article
- An innovative hybrid ANN inception model for detecting and preventing man-in-the-middle attacks in smart networks for shielding sensitive data.Scientific reports · 2026Article
- RNN-based detection of IoT malware using diverse feature engineering methods.Scientific reports · 2026Article
- Malware detection in IoT networks with CNNs and integrated feature engineering.Scientific reports · 2026Article
- Enhancing confidentiality and access control in electronic health record systems using a hybrid hashing blockchain framework.Scientific reports · 2025Article
- An Explainable Hybrid CNN-Transformer Architecture for Visual Malware Classification.Sensors (Basel, Switzerland) · 2025Article
- Design of Low-Cost and Highly Energy-Efficient Convolutional Neural Networks Based on Deterministic Encoding.Sensors (Basel, Switzerland) · 2025Article
- Sparse attention with residual pyramidal depthwise separable convolutional based malware detection with optimization mechanism.Scientific reports · 2024Article
- Detecting DDoS Attacks in the Internet of Medical Things Through Machine Learning Based Classification.The 6th International Conference on Wireless, Intelligent and Distributed Environment for Communication : WIDECOM 2023 : International Conference on Wireless, Intelligent and Distributed Environment for Communication, St. Catharines, ON... · 2024Article
- Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.Cancer medicine · 2023Article
- Artificial intelligence-driven malware detection framework for internet of things environment.PeerJ. Computer science · 2023Article
- Image Sensing and Processing with Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Biserial Miyaguchi-Preneel Blockchain-Based Ruzicka-Indexed Deep Perceptive Learning for Malware Detection in IoMT.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
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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.
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