Zobrazeno 1 - 10
of 214
pro vyhledávání: '"Portmann, Marius"'
Large Language Models (LLMs) have revolutionised natural language processing tasks, particularly as chat agents. However, their applicability to threat detection problems remains unclear. This paper examines the feasibility of employing LLMs as a Net
Externí odkaz:
http://arxiv.org/abs/2408.04342
Autor:
Manocchio, Liam Daly, Layeghy, Siamak, Lo, Wai Weng, Kulatilleke, Gayan K., Sarhan, Mohanad, Portmann, Marius
This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the strengths of transformer models in identifying the long-term behaviour a
Externí odkaz:
http://arxiv.org/abs/2304.14746
Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and lar
Externí odkaz:
http://arxiv.org/abs/2212.08184
Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tas
Externí odkaz:
http://arxiv.org/abs/2212.07558
The performance of machine learning based network intrusion detection systems (NIDSs) severely degrades when deployed on a network with significantly different feature distributions from the ones of the training dataset. In various applications, such
Externí odkaz:
http://arxiv.org/abs/2210.08252
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 compute and energy resources, active security protections are usually mini
Externí odkaz:
http://arxiv.org/abs/2210.03254
Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative samples which is unscalable and computationally prohibitive with a qu
Externí odkaz:
http://arxiv.org/abs/2209.14067
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 automatic forensics. The XG-BoT detector can effectively detect malicious
Externí odkaz:
http://arxiv.org/abs/2207.09088
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 that incorporate graph structures into learning to generalise graph r
Externí odkaz:
http://arxiv.org/abs/2207.06819
Autor:
Layeghy, Siamak, Portmann, Marius
Many of the proposed machine learning (ML) based network intrusion detection systems (NIDSs) achieve near perfect detection performance when evaluated on synthetic benchmark datasets. Though, there is no record of if and how these results generalise
Externí odkaz:
http://arxiv.org/abs/2205.04112