206 citations · 239 across the 14 of their papers we have counts for
15 papers
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba
Zhetong Zhang, Gayan K Kulatilleke
State space models provide efficient linear-complexity processing for long sequences, but their performance frequently degrades as context length grows. We propose SuPER-Mamba, a t…
T5-CSBoost: Adversarial Perturbation Resistant LLM Fingerprinting
Gayan K. Kulatilleke, Mahsa Baktashmotlagh, Siamak Layeghy +1
While many AI-generated text (AIGT) detectors achieve strong performance on clean inputs, their accuracy degrades significantly under light paraphrasing, word substitutions, charac…
MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining
Gayan K. Kulatilleke, Siamak Layeghy, Mahsa Baktashmotlagh +1
We present MambaNetBurst, a compact tokenizer-free byte-level sequence classifier for network burst classification based on a Mamba-2 backbone. In contrast to most recent strong tr…
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…
Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification
Nathasha Naranpanawa, H. Peter Soyer, Adam Mothershaw +4
An ugly duckling is an obviously different skin lesion from surrounding lesions of an individual, and the ugly duckling sign is a criterion used to aid in the diagnosis of cutaneou…
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
Liam Daly Manocchio, Siamak Layeghy, Wai Weng Lo +3
This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the st…
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…
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…