The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

The field of information security is constantly evolving, and new threats appear every day. To ensure that organizations remain protected from these threats, it is important to have a reliable information security audit system. The purpose of this study is to study the current state of information security audit systems in organizations and identify the best practices for the implementation and maintenance of these systems. In the course of the study, various methods and methods of conducting an information security audit will be analyzed, security analysis tools and information security tools.

The evolution of 5G and the anticipated introduction of 6G technologies significantly increases network complexity through service-based architecture, network slicing, virtualization and distributed cloud-native functions. These advancements improve scalability and flexibility but simultaneously expand the attack surface and introduce novel vulnerabilities. Traditional penetration testing methodologies are not suitable for such dynamic and virtualized environments because they rely on static procedures and manual testing that cannot match the speed and structural variability of modern mobile networks. In parallel, machine learning–based intrusion detection systems (IDS) demonstrate strong capabilities in detecting anomalous and zero-day behaviors but operate independently from penetration testing processes. This paper presents an intelligent hybrid method for automated penetration testing in 5G and beyond networks, integrating a machine-learning intrusion detection system based on incremental learning, autoencoders and generative adversarial networks (GANs) – with an attack optimization module driven by the Differential Evolution (DE) algorithm. Instead of a genetic algorithm, DE is employed due to its fast convergence, robustness to local minima, and suitability for optimizing high-dimensional representations of attack strategies. An experimental evaluation on a real OpenAirInterface-based 5G Standalone testbed demonstrates that the DEdriven approach improves vulnerability identification efficiency, produces optimal multi-stage attack strategies, and enables realistic automated penetration scenarios. These results indicate that DE-based optimization provides a scalable, adaptive and efficient foundation for continuous security assessment of next-generation mobile networks.