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AI-Driven Security in 5G and Beyond

University of Washington_091921A
[University of Washington]

Over the past decade, major advances in wireless networking have enabled a variety of Internet of Things (IoT) use cases, greatly facilitating many of the operations in our daily lives. The Internet of Things is only expected to grow with 5G and beyond, which will rely heavily on software-defined networking (SDN) and network functions virtualization (NFV) to deliver the promised quality of service. The proliferation of IoT and the massive attack surface it creates requires intelligent security solutions that enable real-time, automated intrusion detection/mitigation as well as authentication and data integrity protection in these networks. 

Artificial intelligence (AI) tools, especially machine learning (ML) and deep learning (DL), can analyze the massive volumes of network traffic data generated in 5G and beyond networks in real time for anomalies and network security. attacks, and provide effective authentication and data integrity protection mechanisms. When combined with the power of network virtualization and network slicing technologies, AI tools will also play an important role in optimizing the performance of these networks under strict security constraints. 

While there is great potential for harnessing the power of AI to create self-managing networks, adversarial attacks on the algorithms used are an important factor to consider, which can lead to significant performance degradation and disruption of network operations. 

We seek new contributions to address cyber technology security issues for 5G and beyond by leveraging artificial intelligence tools. 

  • ​ML and DL-based based intrusion detection and prevention
  • ML and DL-based authentication and integrity assurance
  • Federated learning for security
  • Adversarial ML in networks
  • ML and DL-based network optimization with security constraints



[More to come ...]

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