Výsledky vyhledávání - "Hong Gunn Chew"

Characterising Network-Connected Devices Using Affiliation Graphs

Autor: Adriel Cheng, Hong Gunn Chew, Kyle Millar, Cheng-Chew Lim

Publikováno v: NOMS

Device management in large networks is of growing importance to network administrators and security analysts alike. The composition of devices on a network can help forecast future traffic demand as well as identify devices that may pose a security r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::113b9236f6393bb4b16afabef01ca828
https://doi.org/10.1109/noms47738.2020.9110309

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Using Convolutional Neural Networks for Classifying Malicious Network Traffic

Autor: Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew

Publikováno v: Deep Learning Applications for Cyber Security ISBN: 9783030130565

As the reliance on the Internet and its constituent applications increase, so too does the value in exploiting these networking systems. Methods to detect and mitigate these threats can no longer rely on singular facets of information, they must be a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::039507326a48bed378e130f6b28ded99
https://doi.org/10.1007/978-3-030-13057-2_5

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Deep Learning for Classifying Malicious Network Traffic

Autor: Kyle Millar, Cheng-Chew Lim, Adriel Cheng, Hong Gunn Chew

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030045029
PAKDD (Workshops)

As the sophistication of cyber malicious attacks increase, so too must the techniques used to detect and classify such malicious traffic in these networks. Deep learning has been deployed in many application domains as it is able to learn patterns fr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ad08c6fd6a5673affd24d93281aa01ac
https://doi.org/10.1007/978-3-030-04503-6_15

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Overview of optimal energy management for nanogrids (end-users with renewables and storage)

Autor: Nesimi Ertugrul, Benjamin Young, Hong Gunn Chew

Publikováno v: 2016 Australasian Universities Power Engineering Conference (AUPEC).

With the uptake of intermittent renewable generation (namely wind and solar), the battery storage market is now growing and generation in modern power systems is becoming more distributed and behind-the-meter. Utilities are investigating ways to prov

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::aea18647dcba2a5544392f35d7bf909a
https://doi.org/10.1109/aupec.2016.7749358

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On regularisation parameter transformation of support vector machines

Autor: Cheng-Chew Lim, Hong Gunn Chew

Publikováno v: Journal of Industrial & Management Optimization. 5:403-415

The Dual-nu Support Vector Machine (SVM) is an effective method in pattern recognition and target detection. It improves on the Dual-C SVM, and offers competitive performance in detection and computation with traditional classifiers. We show that the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fa09318e4db68fae718a3fdd227a9971
https://doi.org/10.3934/jimo.2009.5.403

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An Implementation of Training Dual-nu Support Vector Machines

Autor: Hong Gunn Chew, Cheng-Chew Lim, R.E. Bogner

Publikováno v: Applied Optimization ISBN: 0387242546

Dual-ν Support Vector Machine (2ν-SVM) is a SVM extension that reduces the complexity of selecting the right value of the error parameter selection. However, the techniques used for solving the training problem of the original SVM cannot be directl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::111ac117439671ddb661a1019152fae4
https://doi.org/10.1007/0-387-24255-4_7

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Dual ν-support vector machine with error rate and training size biasing

Autor: Hong-Gunn Chew, Cheng-Chew Lim, R.E. Bogner

Publikováno v: ICASSP
Scopus-Elsevier

Support vector machines (SVMs) have been successfully applied to classification problems. The difficulty in selecting the most effective error penalty has been partly resolved with /spl nu/-SVM. However, the use of uneven training class sizes, which

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9b6297c242945926e6cefd71015b021
https://doi.org/10.1109/icassp.2001.941156

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Conference

Dual ν-support vector machine with error rate and training size biasing.

Autor: Hong-Gunn Chew, Bogner, R.E., Cheng-Chew Lim

Publikováno v: 2001 IEEE International Conference on Acoustics, Speech & Signal Processing. Proceedings (Cat. No.01CH37221); 2001, p1269-1269, 1p

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