Professor’s Research to Be Published in IEEE International Conference Communications
Congratulations to Professor Ibrahim Tamim, Ph.D., B.Eng., whose research on intelligent 5G networks titled “Security and High-Availability while Upholding Network Defense Patterns: The Advantages of A2C in O-RAN VNF Placement” will be published in the IEEE International Conference on Communications (ICC) 2024, in Denver, CO.
ICC is one of two Institute of Electrical and Electronics Engineers (IEEE) Communications Society’s flagship conferences (ICC and Globecom). Each year, close to 2,000 attendees from over 70 countries attend IEEE ICC to engage in robust technical paper sessions, innovative tutorials and workshops, and engaging industry sessions. This 5-day event is known for bringing together audiences from both industry and academia to learn about the latest research and innovations in communications and networking technology, share ideas and best practices, and collaborate on future projects. Past patrons of the conference include Google Cloud, Telus, Nokia, Ericsson, Samsung, and Intel.
The Abstract of Ibrahim’s Research
Next-generation radio access networks such as the Open Radio Access Network (O-RAN) have alleviated many
of the 5G demand and management challenges. However, ORAN’s intelligence, openness, and virtualization have significantly increased the attack surface of RANs. This is specifically dangerous for critical 5G use cases such as Ultra-Reliable and Low-latency Communications (URLLC) due to its strict latency and reliability constraints. In this work, we focus on enhancing the security of the data streams and the security of the ML training and inference hosts in O-RAN URLLC deployments by introducing additional network security functions to O-RAN’s service function chains. Our goal is to maximize the amount of traffic examined by the security functions while adhering to O-RAN’s operational and functional constraints and upholding network defense patterns. Two security function types, encryption Virtualized Network Functions (VNFs) and intrusion detection system VNFs are chosen to achieve this objective. Encryption VNFs provide an additional layer of encryption for data traffic, while IDS VNFs protect the ML training and inference hosts of our solution.
To solve this complex task, an advantage actor-critic deep reinforcement learning agent is developed, which actively allows adaptation to dynamic traffic. We demonstrate that our solution is capable of increasing the number of security functions in URLLC deployments allowing increased data protection and securing its own training and inference hosts.
