@Article{cmes.2024.053379,
AUTHOR = {Ana Hermosilla, Jorge Gallego-Madrid, Pedro Martinez-Julia, Jordi Ortiz, Ved P. Kafle, Antonio Skarmeta},
TITLE = {Advancing 5G Network Applications Lifecycle Security: An ML-Driven Approach},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {141},
YEAR = {2024},
NUMBER = {2},
PAGES = {1447--1471},
URL = {http://www.techscience.com/CMES/v141n2/58147},
ISSN = {1526-1506},
ABSTRACT = {As 5th Generation (5G) and Beyond 5G (B5G) networks become increasingly prevalent, ensuring not only network security but also the security and reliability of the applications, the so-called network applications, becomes of paramount importance. This paper introduces a novel integrated model architecture, combining a network application validation framework with an AI-driven reactive system to enhance security in real-time. The proposed model leverages machine learning (ML) and artificial intelligence (AI) to dynamically monitor and respond to security threats, effectively mitigating potential risks before they impact the network infrastructure. This dual approach not only validates the functionality and performance of network applications before their real deployment but also enhances the network’s ability to adapt and respond to threats as they arise. The implementation of this model, in the shape of an architecture deployed in two distinct sites, demonstrates its practical viability and effectiveness. Integrating application validation with proactive threat detection and response, the proposed model addresses critical security challenges unique to 5G infrastructures. This paper details the model, architecture’s design, implementation, and evaluation of this solution, illustrating its potential to improve network security management in 5G environments significantly. Our findings highlight the architecture’s capability to ensure both the operational integrity of network applications and the security of the underlying infrastructure, presenting a significant advancement in network security.},
DOI = {10.32604/cmes.2024.053379}
}