Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Liu, Shinan"'
Autor:
Chu, Andrew, Jiang, Xi, Liu, Shinan, Bhagoji, Arjun, Bronzino, Francesco, Schmitt, Paul, Feamster, Nick
Many problems in computer networking rely on parsing collections of network traces (e.g., traffic prioritization, intrusion detection). Unfortunately, the availability and utility of these collections is limited due to privacy concerns, data stalenes
Externí odkaz:
http://arxiv.org/abs/2406.02784
Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However, ML-based solutions remain difficult to deploy in practice. Many exist
Externí odkaz:
http://arxiv.org/abs/2402.06099
Autor:
Liu, Shinan, Shaowang, Ted, Wan, Gerry, Chae, Jeewon, Marques, Jonatas, Krishnan, Sanjay, Feamster, Nick
Network traffic analysis increasingly uses complex machine learning models as the internet consolidates and traffic gets more encrypted. However, over high-bandwidth networks, flows can easily arrive faster than model inference rates. The temporal na
Externí odkaz:
http://arxiv.org/abs/2402.03694
Autor:
Jiang, Xi, Liu, Shinan, Gember-Jacobson, Aaron, Bhagoji, Arjun Nitin, Schmitt, Paul, Bronzino, Francesco, Feamster, Nick
Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concerns, such as data staleness. To overcome this limitation, synthetic netw
Externí odkaz:
http://arxiv.org/abs/2310.08543
Accurate and efficient network traffic classification is important for many network management tasks, from traffic prioritization to anomaly detection. Although classifiers using pre-computed flow statistics (e.g., packet sizes, inter-arrival times)
Externí odkaz:
http://arxiv.org/abs/2302.11718
Autor:
Cruz, Stefany, Danek, Logan, Liu, Shinan, Kraemer, Christopher, Wang, Zixin, Feamster, Nick, Huang, Danny Yuxing, Yao, Yaxing, Hester, Josiah
Publikováno v:
Workshop on Usable Security and Privacy (USEC) 2023
Users face various privacy risks in smart homes, yet there are limited ways for them to learn about the details of such risks, such as the data practices of smart home devices and their data flow. In this paper, we present Privacy Plumber, a system t
Externí odkaz:
http://arxiv.org/abs/2301.11998
Video highlight detection is a crucial yet challenging problem that aims to identify the interesting moments in untrimmed videos. The key to this task lies in effective video representations that jointly pursue two goals, \textit{i.e.}, cross-modal r
Externí odkaz:
http://arxiv.org/abs/2206.10157
Autor:
Liu, Shinan, Bronzino, Francesco, Schmitt, Paul, Bhagoji, Arjun Nitin, Feamster, Nick, Crespo, Hector Garcia, Coyle, Timothy, Ward, Brian
Publikováno v:
Proc. ACM Netw., Vol. 1, No. CoNEXT2, Article 7. Publication date: September 2023
Operational networks commonly rely on machine learning models for many tasks, including detecting anomalies, inferring application performance, and forecasting demand. Yet, model accuracy can degrade due to concept drift, whereby the relationship bet
Externí odkaz:
http://arxiv.org/abs/2109.03011
Group activity recognition is a crucial yet challenging problem, whose core lies in fully exploring spatial-temporal interactions among individuals and generating reasonable group representations. However, previous methods either model spatial and te
Externí odkaz:
http://arxiv.org/abs/2108.12630
Autor:
Li, Haopeng, Liu, Lingbo, Yang, Kunlin, Liu, Shinan, Gao, Junyu, Zhao, Bin, Zhang, Rui, Hou, Jun
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos. To model spatial-temporal dependencies of human mobility, we propose a multi-focus Gaussian neighborhood at
Externí odkaz:
http://arxiv.org/abs/2107.08645