ArticleScientific reports2025
A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning.
Article in Scientific reports, 2025. 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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Who cites it
13 citing papers in PubMed.
- Federated ConvNeXt-swin temporal fusion network for malware and botnet detection in IoT systems.Scientific reports · 2026Article
- AI-IoT driven system for agricultural pest outbreak risk prediction.Scientific reports · 2026Article
- Enhanced detection of network intrusions and anomalies in internet of things applications using a hybrid artificial intelligence model combining CNN and LSTM.Scientific reports · 2026Article
- Comparing machine learning and deep learning approaches to predicting the seismic response of slab-column connections.Scientific reports · 2026Article
- An ensemble-based sentiment analysis approach for precision medicine recommendation.Scientific reports · 2026Article
- Performance Analysis of Explainable Deep Learning-Based Intrusion Detection Systems for IoT Networks: A Systematic Review.Sensors (Basel, Switzerland) · 2026Review
- Article
- A cross-domain deep learning framework for remaining useful life prediction in industrial applications.PloS one · 2026Article
- An efficient data driven framework for intrusion detection in wireless sensor networks using deep learning.Scientific reports · 2025Article
- Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments.Scientific reports · 2025Article
- A lightweight framework to secure IoT devices with limited resources in cloud environments.Scientific reports · 2025Article
- AI and IoT-powered edge device optimized for crop pest and disease detection.Scientific reports · 2025Article
- A GAN-Based Approach for enhancing security in satellite based IoT networks using MPI enabled HPC.PloS one · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing use of IoT has brought enormous safety issues that constantly demand stronger hide from increasing risks of intrusions. This paper proposes an Advanced LSTM-CNN Secure Framework to optimize real-time intrusion detection in the IoT context. It adds LSTM layers, which allow for temporal dependencies to be learned, and CNN layers to decompose spatial features which makes this model efficient in identifying threats. It is important to note that the used BoT-IoT dataset involves various cyber attack typologies like DDoS, botnet, reconnaissance, and data exfiltration. These outcomes present that the proposed LSTM-CNN model has 99.87% accuracy, 99.89% precision, and 99.85% recall with a low false positive rate of 0.13% and exceeds CNN, RNN, Standard LSTM, BiLSTM, GRU deep learning models. In addition, the model has 90.2% accuracy in conditions of adversarial attack proving that the model is robust and can be used for practical purposes. Based on feature importance analysis using SHAP, the work finds that packet size, connection duration, and protocol type should be the possible indicators for threat detection. These outcomes suggest that the Hybrid LSTM-CNN model could be useful in improving the security of IoT devices to provide increased reliability with low false alarm rates.
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