206 citations · 239 across the 14 of their papers we have counts for
8 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…
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…
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection
Gayan K. Kulatilleke
Credit cards play an exploding role in modern economies. Its popularity and ubiquity have created a fertile ground for fraud, assisted by the cross boarder reach and instantaneous…
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…
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…