Attack Detection for 5G Networks using AI/ML

The roadmap for 5G networks is already taking shape due to several industrial and academic research efforts. 5G networks are expected to support more diversified services, which should create exciting business opportunities in many vertical sectors. Achieving this requires improving the technologies behind the evolution of 5G and leveraging machine learning (ML)/artificial intelligence (AI) techniques for efficient management of network resources. However, to meet the requirements of various industries, 5G should not only rely on new enabling technologies but also on providing a secure network architecture beyond current network designs. The massive explosion of connected devices which formed what is known as the Internet of Things (IoT) makes this requirement more challenging. To overcome this issue, 5G architecture needs to detect attacks and anomalies before they occur more intelligently. Traditional anomaly detection mechanisms may not scale with the 5G needs. Hence, in this research project, we are motivated to apply machine learning/artificial intelligence techniques to detect attacks and anomalies and thus contribute to designing a secure 5G network. Combining security with artificial intelligence is indispensable nowadays. Indeed, the benefit of the project would be to design and implement innovative and novel techniques for intelligent anomaly detection for 5G networks.

Faculty Supervisor:

Mourad Debbabi

Student:

Partner:

Ericsson Canada Inc (Quebec)

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Concordia University

Program: