A Network Intrusion Detection System (NIDS) serves as the initial line of defence against network attacks that threaten the integrity of data, systems, and networks. Over recent years, Machine Learning (ML) algorithms have been increasingly used in NIDSs to detect malicious traffic due to their remarkable accuracy in identifying malicious network activity.

Nevertheless, ML algorithms are susceptible to Adversarial Machine Learning (AML) attacks, which aim to evade the NIDS with small perturbations of the attack network traffic. This vulnerability has particularly severe consequences, as adversarial attacks pose a substantial threat to overall network security.

While the majority of current research in the field of AML has been directed towards computer vision tasks like image classification and object recognition, there has been a notable increase in interest and activity within the cybersecurity domain. Nevertheless, several challenges persist in this domain, encompassing both performance-related issues and the practicality of applying these methods to real-world scenarios.

The primary objective of this PhD scholarship is to conduct cutting-edge research in the field of AML with a focus on enhancing cybersecurity defences. The selected candidate will explore innovative techniques and methodologies to detect, prevent, and mitigate AML attacks, thereby improving the robustness and resilience of ML-based cybersecurity systems.