405 citations · 1.8k across the 23 of their papers we have counts for
25 papers
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping
Gayan K. Kulatilleke, Marius Portmann, Ryan Ko +1
While Graph Neural Networks have gained popularity in multiple domains, graph-structured input remains a major challenge due to (a) over-smoothing, (b) noisy neighbours (heterophil…
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets
Siamak Layeghy, Marcus Gallagher, Marius Portmann
Network Intrusion Detection Systems (NIDSs) are an increasingly important tool for the prevention and mitigation of cyber attacks. A number of labelled synthetic datasets generated…
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection
Mohanad Sarhan, Siamak Layeghy, Marius Portmann
Machine Learning (ML)-based network intrusion detection systems bring many benefits for enhancing the cybersecurity posture of an organisation. Many systems have been designed and…
Exploring Edge TPU for Network Intrusion Detection in IoT
Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Mohanad Sarhan +2
This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While there are…
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan +2
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can le…
Towards a Standard Feature Set for Network Intrusion Detection System Datasets
Mohanad Sarhan, Siamak Layeghy, Marius Portmann
Network Intrusion Detection Systems (NIDSs) are important tools for the protection of computer networks against increasingly frequent and sophisticated cyber attacks. Recently, a l…