Enabling intrusion detection in 5G networks via novel datasets
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The multitude of services and technologies that 5G incorporates have made modern communication networks very complex and sophisticated in nature. This complexity and the heterogeneity of future networks can increase the vulnerabilities of the network in general. The incorporation of Machine Learning (ML) and Artificial Intelligence (AI) techniques by the attackers enhances the opportunity to reveal such vulnerable points of the network and launch intelligent attacks against the network and network devices. These attacks often traverse undetected due to the lack of intelligent security mechanisms to counter these threats. Therefore, the implementation of real-time, proactive, and self-adaptive security mechanisms throughout the network would be an integral part of 5G as well as future communication systems. Therefore, large amounts of data collected from real networks will play an important role in the training of AI/ML models to identify and detect malicious content in network traffic. This webinar demonstrates 5G-NIDD, a fully labeled dataset built on a functional 5G test network that can be used by those who develop and test AI/ML solutions on intrusion detection.