Generative Adversarial Networks for anomaly detection on decentralised data

Katzef, Marc; Cullen, Andrew C.; Alpcan, Tansu; Leckie, Christopher

doi:10.1016/j.arcontrol.2021.10.002

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The proposed system results compared with the engineering GAN of three representative forms of GANs: i) Jenson Shannon (GAN) [1]; ii) the main difference in l (Wasserstein GAN) [7]; ii) the main difference in l2 (Wasserstein GAN) [9].…”

Section: Resultsmentioning

confidence: 99%

“…When doing weight struncation; the parameters are sheared in the function mappingφζ(x) in the range [0.01, 0.01]. When applying a weight drop to the unit norm l2, use the following rule, p=min{1,1/kpk2}×p described in [9] for each iteration to update some parameter p. The weight decay factor is set to 0.001 for weight loss. On both experiments, the batch size was set to 500.…”

Section: Resultsmentioning

confidence: 99%

“…SAGA makes use of and supports the use of generative adversarial networks (GANs), Deep learning and auto encoders are used to help [7] Where the number of training samples is inadequate or the world described by the initial training data varies over time, synthetic data is generated to retrain machine learning classifiers. Adversarial training is handled by GANs [8] By teaching complex neural network architectures, it is possible to learn the underpinning representations of complex data sources more effectively [9], [10]. The geometric GAN, a new McGAN engineering circular was influenced by the recent discovery that McGAN is made up of three geometries in the function space [11]: − Separate super-level search: This approach focuses on locating the linear classifier's super-level dividing class [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks

Sadkhan

Zaidawi

2022

Bulletin EEI

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The majority of recently developed approaches require a significant number of labelled samples. The proposed system are dedicated to using less marked samples for automatic modulation detection in the cognitive radio domain. The proposed signal classifier generative adversarial nets (GANs) methodology is a semi-supervised learning framework that focuses on adversarial analysis GANs are a major step forward in the development of competitive generative networks, and they've spawned a slew of apparently unrelated versions. The discovery of a single geometric form in GAN and its derivatives is one of the paper's key contributions. In three geometric stages, by demonstrate how to train an adversarial generative model: updating the discriminator parameter away from the separating hyperplane, looking for the separating hyperplane, and updating the generator along the usual vector route of the separating hyperplane. The shortcomings in current approaches are shown by this geometric intuition, leading us to suggest a new geometric GAN formulation that maximizes the margin using SVM separating hyperplane. An equilibrium is reached between the discriminator and generator in the geometric GAN, according to our theoretical research. Furthermore, detailed computational results showing the superior efficiency of the GAN engineering network were obtained.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks

Sadkhan

Zaidawi

2022

Bulletin EEI

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Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction

et al. 2023

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The advent of the Internet of Things and 5G has further accelerated the growth in devices attempting to gain access to the wireless spectrum. A consequence of this has been the commensurate growth in spectrum conflict and congestion across the wireless spectrum, which has begun to impose a significant impost upon innovation in both the public and private sectors. One potential avenue for resolving these issues, and improving the efficiency of spectrum utilisation can be found in devices making intelligent decisions about their access to spectrum through Dynamic Spectrum Allocation. Changing to a system of Dynamic Spectrum Allocation would require the development of complex and sophisticated inference frameworks, that would be able to be deployed at a scale able to support significant numbers of devices. The development and deployment of these systems cannot exist in isolation, but rather would require the development of tools that can simulate, measure, and predict Spectral Occupancy. To support the development such tools, this work reviews not just the available prediction frameworks for networked systems with sparse sensing over large scale geospatial environments, but also holistically considers the myriad of technological approaches required to support Dynamic Spectrum Allocation.

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Generative Adversarial Network Models for Anomaly Detection in Software-Defined Networks

Zacaron,

Lent,

da Silva Ruffo

et al. 2024

J Netw Syst Manage

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Generative Adversarial Networks for anomaly detection on decentralised data

Cited by 7 publications

References 5 publications

Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks

Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks

Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction

Generative Adversarial Network Models for Anomaly Detection in Software-Defined Networks

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