405 citations · 2.1k across the 16 of their papers we have counts for
16 papers
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…
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…
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…
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…
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…
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection
Mohanad Sarhan, Siamak Layeghy, Marius Portmann
Machine Learning (ML)-based network intrusion detection systems bring many benefits for enhancing the cybersecurity posture of an organisation. Many systems have been designed and…
Exploring Edge TPU for Network Intrusion Detection in IoT
Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Mohanad Sarhan +2
This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While there are…
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan +2
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can le…
Towards a Standard Feature Set for Network Intrusion Detection System Datasets
Mohanad Sarhan, Siamak Layeghy, Marius Portmann
Network Intrusion Detection Systems (NIDSs) are important tools for the protection of computer networks against increasingly frequent and sophisticated cyber attacks. Recently, a l…
NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems
Mohanad Sarhan, Siamak Layeghy, Nour Moustafa +1
Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have proven to become a reliable intelligence tool to protect networks against cyberattacks. Network data fe…