Intrusion Detection in IoT Using Deep Learning

Area of Science:

• Computer Science
• Cybersecurity
• Artificial Intelligence

Background:

• Cybersecurity is crucial for diverse applications including IoT devices.
• Existing security methods for IoT face significant challenges.
• Deep learning offers promising solutions for intrusion detection in IoT.

Purpose of the Study:

• To explore and compare deep learning methods for IoT intrusion detection.
• To identify the most effective deep learning model for securing IoT environments.
• To evaluate the performance of CNNs, LSTM, and GRUs for intrusion detection.

Main Methods:

• Implementation of deep learning models: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs).
• Evaluation using a standard dataset for Intrusion Detection in IoT.
• Comparative analysis of proposed models against existing intrusion detection approaches.

Main Results:

• The proposed deep learning method demonstrated the highest accuracy in intrusion detection.
• Empirical results show significant improvements over existing methods.
• Comparative performance analysis highlights the strengths of the evaluated deep learning models.

Conclusions:

• Deep learning, particularly the proposed method, offers a highly effective solution for IoT intrusion detection.
• The study provides valuable insights into selecting optimal deep learning architectures for IoT security.
• Further research can build upon these findings to develop more robust IoT security frameworks.