254 citations · 1.2k across the 27 of their papers we have counts for
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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…