254 citations · 1.2k across the 27 of their papers we have counts for
8 papers · 1 filter
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems
Majed Luay, Siamak Layeghy, Seyedehfaezeh Hosseininoorbin +3
This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Although many previous studies have hi…
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…
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…
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…
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…
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection
Mohanad Sarhan, Siamak Layeghy, Nour Moustafa +1
The uses of Machine Learning (ML) in detection of network attacks have been effective when designed and evaluated in a single organisation. However, it has been very challenging to…
Exploring Deep Neural Networks on Edge TPU
Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Brano Kusy +2
This paper explores the performance of Google's Edge TPU on feed forward neural networks. We consider Edge TPU as a hardware platform and explore different architectures of deep ne…
From Zero-Shot Machine Learning to Zero-Day Attack Detection
Mohanad Sarhan, Siamak Layeghy, Marcus Gallagher +1
The standard ML methodology assumes that the test samples are derived from a set of pre-observed classes used in the training phase. Where the model extracts and learns useful patt…