L2M NSERC - Intrusion Detection System for 5G Network Slices

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Modern communication networks are shifting towards distributed architecture from centralized ones to support the heterogeneous needs of consumers. For example, the 5G network design adopts the microservice architecture, for which each core network function has only one responsibility. These core functions are distributed across the network, which increases the attack surface. Furthermore, this modern network architecture introduces the possibility of an entirely new type of intrusion attack (a.k.a zero-day attack), which has never been seen before. Here, an intrusion attack means unauthorized access to the network, which can be exploited for further data breaches. The use of an intrusion detection system (IDS) is a typical way of detecting intrusion in a network. The existing solutions for intrusion detection fall short in detecting intrusion in a distributed manner. This project aims to advance state-of-the-art IDS solutions to address the challenges introduced by the new architecture of modern networks. Data-driven technologies such as machine learning (ML) have been used to detect zero-day attacks. Our proposed solution will use ML to detect intrusion as early as possible. Furthermore, we plan to use federated learning to apply our solution in a distributed manner across the network. Federated Learning is a technique that enables a ML model to learn from a broader range of data that is distributed across different locations. By combining early detection with Federated Learning, our project seeks to reduce the data movement from the distributed servers to the central data center of a network to facilitate the development of effective IDSs.