206 citations · 239 across the 14 of their papers we have counts for
7 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…
Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data
Gayan K. Kulatilleke, Sugandika Samarakoon
With growing credit card transaction volumes, the fraud percentages are also rising, including overhead costs for institutions to combat and compensate victims. The use of machine…
Credit card fraud detection - Classifier selection strategy
Gayan K. Kulatilleke
Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect fraud…
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…
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…