405 citations · 2.6k across the 32 of their papers we have counts for
32 papers
SIREN (Luring LLMs onto the Rocks): PAIR-Driven Preference Manipulation in Web-RAG Recommenders
Evan Caville, Spencer Kayser, Siamak Layeghy +3
This paper investigates the adversarial manipulation of the ranked recommendations produced by web-augmented large language models (LLMs). When an LLM answers a recommendation quer…
LoRIS: LoRaWAN-based IoT Platform for Sustainability Monitoring in Hotels
Yash Pandey, Angus Gray, Reza Serati +9
The hospitality sector is a major source of global greenhouse gas emissions, water stress, and waste generation, yet sustainability reporting in hotels remains constrained by coars…
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…
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…
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).…
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…
P4-NIDS: High-Performance Network Monitoring and Intrusion Detection in P4
Yaying Chen, Siamak Layeghy, Liam Daly Manocchio +1
This paper presents a high-performance, scalable network monitoring and intrusion detection system (IDS) implemented in P4. The proposed solution is designed for high-performance e…
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…
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…
On Generalisability of Machine Learning-based Network Intrusion Detection Systems
Siamak Layeghy, Marius Portmann
Many of the proposed machine learning (ML) based network intrusion detection systems (NIDSs) achieve near perfect detection performance when evaluated on synthetic benchmark datase…
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…
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…
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…
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks
Mohanad Sarhan, Siamak Layeghy, Nour Moustafa +2
A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network interr…