254 citations · 1.2k across the 27 of their papers we have counts for
12 papers · 1 filter
NBC-Softmax : Darkweb Author fingerprinting and migration tracking
Gayan K. Kulatilleke, Shekhar S. Chandra, Marius Portmann
Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, w…
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection
Mohanad Sarhan, Gayan Kulatilleke, Wai Weng Lo +2
Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performa…
DI-NIDS: Domain Invariant Network Intrusion Detection System
Siamak Layeghy, Mahsa Baktashmotlagh, Marius Portmann
The performance of machine learning based network intrusion detection systems (NIDSs) severely degrades when deployed on a network with significantly different feature distribution…
Network Intrusion Detection System in a Light Bulb
Liam Daly Manocchio, Siamak Layeghy, Marius Portmann
Internet of Things (IoT) devices are progressively being utilised in a variety of edge applications to monitor and control home and industry infrastructure. Due to the limited comp…
Efficient block contrastive learning via parameter-free meta-node approximation
Gayan K. Kulatilleke, Marius Portmann, Shekhar S. Chandra
Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative…
XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics
Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan +2
In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model comprises a botnet detector and an explainer for auto…
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks
Evan Caville, Wai Weng Lo, Siamak Layeghy +1
This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data…
On Generalisability of Machine Learning-based Network Intrusion Detection Systems
Siamak Layeghy, Marius Portmann
Many of the proposed machine learning (ML) based network intrusion detection systems (NIDSs) achieve near perfect detection performance when evaluated on synthetic benchmark datase…
SCGC : Self-Supervised Contrastive Graph Clustering
Gayan K. Kulatilleke, Marius Portmann, Shekhar S. Chandra
Graph clustering discovers groups or communities within networks. Deep learning methods such as autoencoders (AE) extract effective clustering and downstream representations but ca…
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection
Mohanad Sarhan, Wai Weng Lo, Siamak Layeghy +1
The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The…
Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin
Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan +2
Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transf…
Graph Neural Network-based Android Malware Classification with Jumping Knowledge
Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan +2
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set o…