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AI-Driven Security in 5G and Beyond

University of Washington_091921A
[University of Washington]

- Overview

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 IoT 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 AI 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

- AI-driven Security Systems

AI-driven security systems can help protect 5G networks by continuously monitoring network traffic, analyzing patterns, and identifying anomalies that may indicate potential threats. 

AI tools, such as machine learning (ML) and deep learning (DL), can help analyze large amounts of network traffic data to discover anomalies and cyber-attacks. ML algorithms can also continuously learn from network data, predict issues, and ensure seamless network performance.

5G and beyond networks will rely heavily on AI to enable fully autonomous management capabilities, such as self-configuration, self-optimization, self-healing, and self-protection. This makes AI an attractive target for attackers. 

Some examples of AI in cybersecurity include: Breach, phishing, and malware detection, Spam filtering, Bot identification, Thread intelligence, Vulnerability management, Incident response, Fraud detection, Network segmentation. 

Some security risks of AI include: 

  • Data poisoning and manipulation
  • Automated malware
  • Impersonation and hallucination abuse


[More to come ...]

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