Výsledky vyhledávání

Akademický článek

DeepCoAST: Unveiling Split Trace Correlation to Counter Traffic Splitting Defenses

Autor: Goun Kim, Hyeonjeong Kwak, Sujin Kim, Youhee Park, Jihyeun Park, Se Eun Oh

Publikováno v: IEEE Access, Vol 12, Pp 158266-158281 (2024)

Despite its widespread adoption, Tor remains vulnerable to traffic analysis attacks, which enables both ends of the communication to be inferred by network-level adversaries. Notable examples of such attacks include website fingerprinting and end-to-

Externí odkaz: https://doaj.org/article/104edc08c8594dd39a6baee571711b26

Zobrazit plný text záznamu

Akademický článek

Cooperative inhibition of RIP1-mediated NF-κB signaling by cytomegalovirus-encoded deubiquitinase and inactive homolog of cellular ribonucleotide reductase large subunit.

Autor: Ki Mun Kwon, Se Eun Oh, Young Eui Kim, Tae-Hee Han, Jin-Hyun Ahn

Publikováno v: PLoS Pathogens, Vol 13, Iss 6, p e1006423 (2017)

Several viruses have been found to encode a deubiquitinating protease (DUB). These viral DUBs are proposed to play a role in regulating innate immune or inflammatory signaling. In human cytomegalovirus (HCMV), the largest tegument protein encoded by

Externí odkaz: https://doaj.org/article/d640a82cf7f548f2826828ba5063e37a

Zobrazit plný text záznamu

GANDaLF: GAN for Data-Limited Fingerprinting

Autor: Nicholas Hopper, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright, Se Eun Oh

Publikováno v: Proceedings on Privacy Enhancing Technologies, Vol 2021, Iss 2, Pp 305-322 (2021)

We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81a74d54ff783f07f166e58e4c0af0b2
https://doaj.org/article/925f5192af9f4da1bfe7ccd2e9d72b37

Zobrazit plný text záznamu

DeepCoFFEA: Improved Flow Correlation Attacks on Tor via Metric Learning and Amplification

Autor: Se Eun Oh, Taiji Yang, Nate Mathews, James K Holland, Mohammad Saidur Rahman, Nicholas Hopper, Matthew Wright

Publikováno v: 2022 IEEE Symposium on Security and Privacy (SP).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9c5f00f82ee52740ed6d43f3b2410dbf
https://doi.org/10.1109/sp46214.2022.9833801

Zobrazit plný text záznamu

p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

Autor: Se Eun Oh, Saikrishna Sunkam, Nicholas Hopper

Publikováno v: Proceedings on Privacy Enhancing Technologies, Vol 2019, Iss 3, Pp 191-209 (2019)

Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f695342cb4c34ce11b5e31df0e786c09
https://doaj.org/article/c678e252c31148a7b3ae7952ab93dede

Zobrazit plný text záznamu

Fingerprinting Keywords in Search Queries over Tor

Autor: Se Eun Oh, Nicholas Hopper, Shuai Li

Publikováno v: Proceedings on Privacy Enhancing Technologies, Vol 2017, Iss 4, Pp 251-270 (2017)

Search engine queries contain a great deal of private and potentially compromising information about users. One technique to prevent search engines from identifying the source of a query, and Internet service providers (ISPs) from identifying the con

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcbc13ff50b335af386ce3ac908abbf8
https://doi.org/10.1515/popets-2017-0048

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání