405 citations · 2.6k across the 32 of their papers we have counts for
10 papers · 1 filter
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…
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets
Siamak Layeghy, Marcus Gallagher, Marius Portmann
Network Intrusion Detection Systems (NIDSs) are an increasingly important tool for the prevention and mitigation of cyber attacks. A number of labelled synthetic datasets generated…
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…