IOT-BASED VULNERABILITIES FRAMEWORK FOR 5G NETWORK

Abstract

The rapid growth of Internet of Things (IoT) devices and 5G networks has created new opportunities for smart services in healthcare, transportation, manufacturing, and smart cities. However, this advancement has also increased security risks because many IoT devices have limited processing power and storage, making them vulnerable to cyberattacks such as DDoS, spoofing, malware, unauthorized access, and data interception. This thesis proposes a layered security framework to improve the protection of IoT-enabled 5G networks. The framework covers the IoT device layer, access network layer, 5G core network, MEC layer, and cloud layer. It uses machine learning-based intrusion detection at the edge layer, blockchain for data integrity and trust management, and encryption and authentication for secure communication. The framework is evaluated using detection accuracy, latency, throughput, and packet loss. The results show that intelligent edge-based security can improve threat detection while maintaining efficient network performance.