A Way to Deduct the Attacks which is Anomaly in Nature in 5G Networks with the Use of Machine Learning

A formidable task arises in addressing the constantly evolving challenges brought about by the growing threats to cybersecurity, the expanded attack surfaces brought about by the Internet of Things (IoT), increased network diversity, and the increasing prevalence of virtualization techniques and distributed architectures. In order to create an automated, flexible, and scalable network defence system, this paper presents the idea of software-defined security, or SDS. SDS takes use of recent developments in machine learning by using Convolutional neural networks, more commonly that are trained using Neural Architecture Look to detect anomalous network traffic. A complete defence system is intended for 5G networks by incorporating SDS into a system to detect intrusions. Experiments were carried out using a CNN to gather and analyse normal and abnormal network traffic in a computerised setting in order to support this assertion. The results show that the model is notably effective at correctly classifying harmless traffic and identifying abnormal traffic (96.4 %). This demonstrates how well network flow analysis can identify prevalent harmful attacks and offers a workable method for recognising malicious network data that is encrypted. SDS is essentially a viable approach that shows promise for strengthening networks against the changing cybersecurity environment and improving overall network security.