Enhancing security in 5G edge networks: predicting real-time zero trust attacks using machine learning in SDN environments

• Autor(a) principal: Ashfaq, Fiza
• Data de Publicação: 2025
• Outros Autores: Wasim, Muhammad, Shah, Mumtaz Ali, Ahad, Abdul, Pires, Ivan Miguel
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/10773/44564
• topic: Cyber security; SDN; Machine learning; Zero trust; Real-time; Intrusion detection; Intrusion prevention

The Internet has been vulnerable to several attacks as it has expanded, including spoofing, viruses, malicious code attacks, and Distributed Denial of Service (DDoS). The three main types of attacks most frequently reported in the current period are viruses, DoS attacks, and DDoS attacks. Advanced DDoS and DoS attacks are too complex for traditional security solutions, such as intrusion detection systems and firewalls, to detect. The combination of machine learning methods with AI-based machine learning has led to the introduction of several novel attack detection systems. Due to their remarkable performance, machine learning models, in particular, have been essential in identifying DDoS attacks. However, there is a considerable gap in the work on real-time detection of such attacks. This study uses Mininet with the POX Controller to simulate an environment to detect DDoS attacks in real-time settings. The CICDDoS2019 dataset identifies and classifies such attacks in the simulated environment. In addition, a virtual software-defined network (SDN) is used to collect network information from the surrounding area. When an attack occurs, the pre-trained models are used to analyze the traffic and predict the attack in real-time. The performance of the proposed methodology is evaluated based on two metrics: accuracy and detection time. The results reveal that the proposed model achieves an accuracy of 99% within 1 s of the detection time.