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Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems
Engy El-Shafeiy1, Walaa M Elsayed2, Haitham Elwahsh3
1Department of Computer Science, Faculty of Computers & Artificial Intelligence, University of Sadat City, Sadat City 32897, Egypt.
Sensors (Basel, Switzerland)
|September 28, 2024
Summary
A new deep learning intrusion detection system (IDS), DCGR_IoT, effectively secures Internet of Things (IoT) networks. It achieves 99.2% accuracy in detecting cyber-attacks by analyzing network traffic patterns.
Area of Science:
- Cybersecurity
- Network Security
- Artificial Intelligence
Background:
- The rapid expansion of the Internet of Things (IoT) necessitates advanced security solutions.
- Traditional Intrusion Detection Systems (IDS) struggle with the unique challenges of IoT environments, including device diversity and real-time detection needs.
Purpose of the Study:
- To propose DCGR_IoT, a novel deep neural learning-based IDS designed for bidirectional IoT communication networks.
- To enhance anomaly detection capabilities within IoT environments.
Main Methods:
- Utilizing Convolutional Neural Networks (CNN) for spatial feature extraction and data filtering.
- Employing Complex Gated Recurrent Networks (CGRNs) for temporal feature extraction and multidimensional feature subset construction.
- Leveraging CGRNs to create detailed spatial representations of network traffic for critical feature extraction.
Main Results:
- DCGR_IoT demonstrated high effectiveness on benchmark datasets (UNSW-NB15, KDDCup99, IoT-23).
- Achieved a superior detection accuracy rate of 99.2% against sophisticated cyber-attacks.
- Validated the system's capability for efficient and accurate intrusion detection in IoT networks.
Conclusions:
- DCGR_IoT presents a robust and effective solution for safeguarding IoT networks.
- The proposed system addresses the limitations of conventional IDS in dynamic IoT environments.
- Highlights the potential of deep learning models for advanced IoT security.
