254 citations · 1.2k across the 27 of their papers we have counts for
13 papers · 1 filter
LLMs for Zero-Shot Threat Detection via Structured Risk Indicators
Abdullah Alghamdi, Siamak Layeghy, Marius Portmann
We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from heterogeneous security logs. The…
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…
eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
Paul R. B. Houssel, Siamak Layeghy, Priyanka Singh +1
This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems (NIDS) by leveraging Large Language Models (LLMs).…
Towards Explainable Network Intrusion Detection using Large Language Models
Paul R. B. Houssel, Priyanka Singh, Siamak Layeghy +1
Large Language Models (LLMs) have revolutionised natural language processing tasks, particularly as chat agents. However, their applicability to threat detection problems remains u…
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…
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…
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…
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…
Feature Analysis for Machine Learning-based IoT Intrusion Detection
Mohanad Sarhan, Siamak Layeghy, Marius Portmann
Internet of Things (IoT) networks have become an increasingly attractive target of cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to implement netwo…