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A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning.

Priyanshu Sinha1, Dinesh Sahu2, Shiv Prakash3

  • 1Department of Electronics and Communication, University of Allahabad, Prayag Raj, Uttar Pradesh, India.

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Summary

This study introduces an Advanced LSTM-CNN framework for real-time IoT intrusion detection. The model achieves high accuracy and robustness against cyber threats, improving IoT device security.

Keywords:
CybersecurityDeep learningHybrid LSTM-CNNIntrusion detectionIoT securityMachine learningThreat detection

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • The proliferation of Internet of Things (IoT) devices has introduced significant security vulnerabilities, necessitating advanced intrusion detection systems.
  • Existing deep learning models face challenges in effectively identifying diverse cyber threats in real-time IoT environments.

Purpose of the Study:

  • To propose and evaluate an Advanced Long Short-Term Memory (LSTM)-Convolutional Neural Network (CNN) Secure Framework for optimizing real-time intrusion detection in IoT networks.
  • To enhance the identification of various cyber attack typologies, including Distributed Denial of Service (DDoS), botnet, reconnaissance, and data exfiltration.

Main Methods:

  • Development of a hybrid LSTM-CNN model integrating LSTM layers for temporal dependency learning and CNN layers for spatial feature decomposition.
  • Utilizing the BoT-IoT dataset, which encompasses a wide range of cyber attack scenarios.
  • Performance evaluation against other deep learning models (CNN, RNN, Standard LSTM, BiLSTM, GRU) and assessment of robustness against adversarial attacks using SHAP for feature importance analysis.

Main Results:

  • The proposed LSTM-CNN model achieved high performance metrics: 99.87% accuracy, 99.89% precision, and 99.85% recall, with a low false positive rate of 0.13%.
  • The model demonstrated superior performance compared to conventional deep learning models.
  • Achieved 90.2% accuracy under adversarial attack conditions, indicating significant robustness.
  • Feature importance analysis identified packet size, connection duration, and protocol type as key indicators for threat detection.

Conclusions:

  • The Advanced LSTM-CNN Secure Framework offers a robust and efficient solution for real-time intrusion detection in IoT environments.
  • The model's high accuracy, precision, and recall, coupled with its resilience to adversarial attacks, make it suitable for practical security applications.
  • The findings suggest that this hybrid approach can significantly enhance IoT device security by providing reliable threat detection with minimal false alarms.