Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks

Area of Science:

• Computer Science
• Network Security

Background:

• The widespread adoption of the Internet of Things (IoT) creates a vast attack surface.
• Many IoT devices lack the resources for robust security protocols, making them vulnerable to malware.
• Existing 4G and 5G network designs require enhanced security mechanisms to mitigate IoT-related threats.

Purpose of the Study:

• To propose a novel Software-Defined Networking (SDN)-based architecture for identifying and isolating compromised IoT devices.
• To enhance the security of 4G and 5G networks against attacks originating from IoT devices.
• To enable early threat detection and limit the impact of Distributed Denial of Service (DDoS) attacks.

Main Methods:

• Development of an SDN-based architecture for network traffic analysis.
• Implementation of a mechanism to identify suspicious IoT nodes based on their network behavior.
• Redirection of suspicious traffic to a dedicated network slice for in-depth analysis.
• Ensuring interoperability with existing 4G and 5G network deployments.

Main Results:

• The proposed architecture effectively identifies suspicious IoT nodes.
• Traffic from compromised devices can be isolated and analyzed without disrupting legitimate network services.
• Early detection of potential threats, including DDoS attacks, is achieved.
• The solution is designed for easy integration into current network infrastructures.

Conclusions:

• The SDN-based architecture offers a viable solution for securing 4G/5G networks against IoT-related security threats.
• Early detection and isolation of compromised IoT devices are crucial for preventing large-scale network attacks.
• The proposed system enhances network resilience and security in the era of massive IoT deployment.