A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning
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 te...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94500-5 |
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| author | Priyanshu Sinha Dinesh Sahu Shiv Prakash Tiansheng Yang Rajkumar Singh Rathore Vivek Kumar Pandey |
| author_facet | Priyanshu Sinha Dinesh Sahu Shiv Prakash Tiansheng Yang Rajkumar Singh Rathore Vivek Kumar Pandey |
| author_sort | Priyanshu Sinha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fe9dfe6970394afeb57f4315044b57a5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fe9dfe6970394afeb57f4315044b57a52025-08-20T02:41:33ZengNature PortfolioScientific Reports2045-23222025-03-0115112610.1038/s41598-025-94500-5A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learningPriyanshu Sinha0Dinesh Sahu1Shiv Prakash2Tiansheng Yang3Rajkumar Singh Rathore4Vivek Kumar Pandey5Department of Electronics and Communication, University of AllahabadSCSET, Bennett UniversityDepartment of Electronics and Communication, University of AllahabadPontypridd, University of South WalesCardiff School of Technologies, Cardiff Metropolitan UniversityDepartment of Electronics and Communication, University of AllahabadAbstract 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.https://doi.org/10.1038/s41598-025-94500-5IoT securityDeep learningHybrid LSTM-CNNIntrusion detectionCybersecurityThreat detection |
| spellingShingle | Priyanshu Sinha Dinesh Sahu Shiv Prakash Tiansheng Yang Rajkumar Singh Rathore Vivek Kumar Pandey A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning Scientific Reports IoT security Deep learning Hybrid LSTM-CNN Intrusion detection Cybersecurity Threat detection |
| title | A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning |
| title_full | A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning |
| title_fullStr | A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning |
| title_full_unstemmed | A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning |
| title_short | A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning |
| title_sort | high performance hybrid lstm cnn secure architecture for iot environments using deep learning |
| topic | IoT security Deep learning Hybrid LSTM-CNN Intrusion detection Cybersecurity Threat detection |
| url | https://doi.org/10.1038/s41598-025-94500-5 |
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