1 code implementation • 28 Apr 2023 • Liam Daly Manocchio, Siamak Layeghy, Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan, Marius Portmann
This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs).
no code implementations • 15 Dec 2022 • Mohanad Sarhan, Gayan Kulatilleke, Wai Weng Lo, Siamak Layeghy, Marius Portmann
Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples.
no code implementations • 15 Oct 2022 • Siamak Layeghy, Mahsa Baktashmotlagh, Marius Portmann
In order to enhance the generalisibility of machine learning based network intrusion detection systems, we propose to extract domain invariant features using adversarial domain adaptation from multiple network domains, and then apply an unsupervised technique for recognising abnormalities, i. e., intrusions.
no code implementations • 6 Oct 2022 • 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.
no code implementations • 19 Jul 2022 • Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan, Siamak Layeghy, Marius Portmann
The proposed model comprises a botnet detector and an explainer for automatic forensics.
1 code implementation • 14 Jul 2022 • Evan Caville, Wai Weng Lo, Siamak Layeghy, Marius Portmann
This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection.
no code implementations • 9 May 2022 • Siamak Layeghy, Marius Portmann
Our investigation also indicates that overall, unsupervised learning methods generalise better than supervised learning models in our considered scenarios.
no code implementations • 8 Apr 2022 • Mohanad Sarhan, Wai Weng Lo, Siamak Layeghy, Marius Portmann
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.
no code implementations • 20 Mar 2022 • Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan, Siamak Layeghy, Marius Portmann
The proposed method was evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in the detection of illicit cryptocurrency transactions.
no code implementations • 19 Jan 2022 • Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK).
no code implementations • 4 Nov 2021 • Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, Marius Portmann
The framework has been designed and evaluated in this paper by using two key datasets in a NetFlow format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2.
no code implementations • 17 Oct 2021 • Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Brano Kusy, Raja Jurdak, Marius Portmann
Based on the use of a joint-time-frequency data representation, also known as spectrogram, we explore the trade-off between classification performance and the energy consumed for inference.
no code implementations • 30 Sep 2021 • Mohanad Sarhan, Siamak Layeghy, Marcus Gallagher, Marius Portmann
The standard ML methodology assumes that the test samples are derived from a set of pre-observed classes used in the training phase.
no code implementations • 29 Aug 2021 • Mohanad Sarhan, Siamak Layeghy, Marius Portmann
This demonstrates a significant potential to reduce the computational and storage cost of intrusion detection systems while maintaining near-optimal detection accuracy.
no code implementations • 28 Aug 2021 • Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, Marcus Gallagher, Marius Portmann
In an analysis of related works, it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction (FR) and Machine Learning (ML) techniques on NIDS datasets.
no code implementations • 19 Apr 2021 • Siamak Layeghy, Marcus Gallagher, Marius Portmann
Our results show that the two real-world datasets are quite similar among themselves in regards to most of the considered statistical features.
no code implementations • 15 Apr 2021 • Mohanad Sarhan, Siamak Layeghy, Marius Portmann
Therefore, two feature sets (NetFlow and CICFlowMeter) have been evaluated in terms of detection accuracy across three key datasets, i. e., CSE-CIC-IDS2018, BoT-IoT, and ToN-IoT.
2 code implementations • 30 Mar 2021 • Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
no code implementations • 30 Mar 2021 • Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Mohanad Sarhan, Raja Jurdak, Marius Portmann
The focus of this paper is the exploration of deep learning-based NIDS at the edge of IoT, and in particular the computational and energy efficiency.
no code implementations • 6 Nov 2020 • Seyedeh Faezeh Hosseini Noorbin, Siamak Layeghy, Brano Kusy, Raja Jurdak, Greg Bishop-hurley, Marius Portmann
The key results of this paper is that the joint time-frequency data representation, even when used in conjunction with a relatively basic neural network classifier, can outperform the best cattle activity classifiers reported in the literature.