ArticleSensors (Basel, Switzerland)2023
CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 94 citations in OpenAlex.
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- Application of CW-RNN model in network attack detection and its promotion to the governance of international law.Scientific reports · 2026Article
- Safeguarding against external intrusions utilizing adaptive bio-inspired multi-population anomaly detection for IoT network.PloS one · 2026Article
- (IoT) Network intrusion detection system using optimization algorithms.Scientific reports · 2025Article
- The data analysis of sports training by ID3 decision tree algorithm and deep learning.Scientific reports · 2025Article
- Detection and classification of microplastics in green tea using SERS with gold nanoparticle substrates integrating chemometrics and deep learning.Current research in food science · 2025Article
- A GAN-Based Approach for enhancing security in satellite based IoT networks using MPI enabled HPC.PloS one · 2025Article
- A hybrid AI based framework for enhancing security in satellite based IoT networks using high performance computing architecture.Scientific reports · 2024Article
- MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba.Sensors (Basel, Switzerland) · 2024Article
- Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks.Sensors (Basel, Switzerland) · 2023Article
- Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis.Sensors (Basel, Switzerland) · 2023Article
- Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
The Internet of Things (IoT) has brought significant advancements that have connected our world more closely than ever before. However, the growing number of connected devices has also increased the vulnerability of IoT networks to several types of attacks. In this paper, we present an approach for detecting attacks on IoT networks using a combination of two convolutional neural networks (CNN-CNN). The first CNN model is leveraged to select the significant features that contribute to IoT attack detection from the raw data on network traffic. The second CNN utilizes the features identified by the first CNN to build a robust detection model that accurately detects IoT attacks. The proposed approach is evaluated using the BoT IoT 2020 dataset. The results reveal that the proposed approach achieves 98.04% detection accuracy, 98.09% precision, 99.85% recall, 98.96% recall, and a 1.93% false positive rate (FPR). Furthermore, the proposed approach is compared with other deep learning algorithms and feature selection methods; the results show that it outperforms these algorithms.
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