Výsledky vyhledávání - "Adriel Cheng"

From traffic classes to content: A hierarchical approach for encrypted traffic classification

Autor: Ying Li, Yi Huang, Suranga Seneviratne, Kanchana Thilakarathna, Adriel Cheng, Guillaume Jourjon, Darren Webb, David B. Smith, Richard Yi Da Xu

The vast majority of Internet traffic is now end-to-end encrypted, and while encryption provides user privacy and security, it has made network surveillance an impossible task. Various parties are using this limitation to distribute problematic conte

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dddd9ca155e0d44a404b4af7727c507a
https://hdl.handle.net/10453/169462

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Task adaptive siamese neural networks for open-set recognition of encrypted network traffic with bidirectional dropout

Autor: Yi Huang, Ying Li, Timothy Heyes, Guillaume Jourjon, Adriel Cheng, Suranga Seneviratne, Kanchana Thilakarathna, Darren Webb, Richard Yi Da Xu

Existing deep learning approaches have achieved high performance in encrypted network traffic analysis tasks. However, practical requirements such as open-set recognition on dynamically changing tasks (e.g., changes in the target website list), chall

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b664c4fbc531db8186c1c2504978e60d
https://hdl.handle.net/10453/168095

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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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PAC-GAN: Packet Generation of Network Traffic using Generative Adversarial Networks

Autor: Adriel Cheng

Publikováno v: 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

Generative adversarial networks (GANs) have proven extremely successful in creating artificial yet highly realistic media data such as images, text, audio and videos. In this paper, we adapt and describe a GAN method for creating network traffic data

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c6debede16b9577262388231b2d3e607
https://doi.org/10.1109/iemcon.2019.8936224

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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 Content: Unveiling Video Streaming Content from Encrypted WiFi Traffic

Autor: Richard Yi Da Xu, Ying Li, Guillaume Jourjon, Adriel Cheng, Darren Webb, Yi Huang, Suranga Seneviratne, Kanchana Thilakarathna

Publikováno v: NCA

© 2018 IEEE. The proliferation of smart devices has led to an exponential growth in digital media consumption, especially mobile video for content marketing. The vast majority of the associated Internet traffic is now end-to-end encrypted, and while

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1045bd56feb3ddb83676c83734e057be
https://doi.org/10.1109/nca.2018.8548317

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