Ai And Machine Learning for Cyber Threat Intelligence Sharing in SD-WAN Networks
Keywords:
Artificial Intelligence, Cyber Threat Intelligence, SD-WAN, Machine LearningAbstract
The increasing use of Software-Defined Wide Area Networks (SD-WAN) in corporate settings has resulted in substantial advantages in terms of efficiency, adaptability, and scalability. Cybersecurity threats have emerged, however, due to the ever-changing and dispersed character of the network architecture. An essential method for proactively identifying, avoiding, and reacting to cyber assaults is the sharing of cyber threat intelligence (CTI). This paper explores the integration of AI and ML approaches into CTI sharing scenarios within the framework of software-defined wide area networks (SD-WAN). Organisations may automate threat information collecting, analysis, and dissemination across remote nodes in real time with the use of artificial intelligence and machine learning. This study explores supervised and unsupervised learning models for analysing behaviour, detecting threats, and identifying anomalies. Furthermore, we look at the potential of federated learning to keep data private across several SD-WAN locations. Along with this, the report delves into the challenges that come with intelligence sharing across several organisations, touching on topics like trust, data standards, and interoperability. Evidence from experiments and case studies shows that CTI sharing enhanced with AI and ML may reduce reaction times and increase danger detection rates. The results illuminate the potential of intelligent threat-sharing systems in terms of strengthening the cyber resilience of SD-WAN installations and allowing defensive mechanisms that are more adaptable, self-sufficient, and cooperative.
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