Detecting Denial Attacks in Malicious Devices Through Analysis of Heterogeneous Networks

The advent of 5G networks has transformed wireless communication with high-speed, low-latency connectivity, enabling a surge in IoT deployments across various sectors. However, this rapid growth has also expanded the attack surface, making 5G networks increasingly vulnerable to security threats such as Distributed Denial-of-Service (DDoS) attacks. This paper examines the impact of DDoS attacks on 5G network performance, particularly in terms of throughput degradation and blocking probability. We propose an adaptive framework that classifies users based on performance metrics such as throughput, delay, and latency to dynamically allocate resources and prioritize legitimate traffic. The framework incorporates a queuing-based channel allocation mechanism to isolate malicious nodes, ensuring uninterrupted service for critical applications like remote surgery and autonomous vehicles. Comparative simulations reveal that 5G networks demonstrate greater resilience than 4G LTE, maintaining lower blocking probabilities and experiencing only minimal throughput loss (a 10 Mbps drop from a 12 Mbps baseline under 20 attack nodes), whereas 4G suffers more severe degradation. These findings underscore the need for intelligent, adaptive security architectures leveraging Network Function Virtualization (NFV), Software-Defined Networking (SDN), and AI-based monitoring to safeguard 5G infrastructures against evolving DDoS threats.