Analysis of Authentication Protocols in 5G Mobile Networks an‎d Intrusion Detection Using Artificial Intelligence

10/22/2026 12:00:00 AM

We aim to investigate DDoS attacks in 5G networks. Our primary goal is to design a smarter model that consumes fewer resources. To achieve this, we plan to use a hybrid model an‎d eva‎luate the impact of this architecture on our work. This approach combines machine learning an‎d deep learning models. In the proposed architecture, the machine learning model, which uses fewer resources, initially analyzes the traffic. Suspicious traffic is then forwarded to deep learning models for further examination. Conventional attack detection models rely solely on deep learning methods, an‎d since most network traffic is benign, this leads to significant resource waste as the model continuously monitors all network traffic. Lightweight models lack the ability to classify attacks an‎d can only assist in initial detection. These models may also produce errors, which deep learning models can accurately analyze an‎d correct. For instance, if the machine learning model misidentifies benign traffic as an attack, the deep learning model can rectify this error.

سيستم تشخيص نفوذ , شبكه‌ هاي نسل پنجم , مدل هيبريدي , يادگيري ماشين , يادگيري عميق

IDS , 5G , Hybrid model , Machine learning , Deep learning

Seyed Mohammad Hosein Mousavi far

Dr. Hadi Shahriar Shahhoseini