Joo Yeon Cho's research while affiliated with ADVA Optical Networking SE and other places
What is this page?
This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
Publications (5)
Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to billons of users worldwide, are prone to a variety of anomalies resulting from hard failures (e.g., fiber cuts) and malicious physical attacks [e.g., optical eaves...
Machine learning (ML) has recently emerged as a powerful tool to enhance the proactive optical network maintenance and thereby improves network reliability and reduces unplanned downtime and maintenance costs. However, it is challenging to develop an accurate and reliable ML model for solving predictive maintenance tasks (e.g., anomaly detection ,...
Secure and reliable data communication in optical networks is critical for high-speed internet. We propose a data driven approach for the anomaly detection and faults identification in optical networks to diagnose physical attacks such as fiber breaks and optical tapping. The proposed methods include an autoencoder-based anomaly detection and an at...
Building a reliable and accurate machine learning (ML) model is challenging
in optical networks when training datasets are business-sensitive. We propose
a framework of secure collaborative ML learning for predictive maintenance on
cross-vendor datasets. Our framework is based on federated learning and multi-party
computation technologies. Each ven...
Building a reliable and accurate machine learning (ML) model is challenging in optical networks when training datasets are business-sensitive. We propose a framework of secure collaborative ML learning for predictive maintenance on cross-vendor datasets. Our framework is based on federated learning and multi-party computation technologies. Each ven...
Citations
... To overcome the limitations of supervised learning (SL), unsupervised learning (UL) techniques (e.g., clustering, density estimation), were instead applied to identify abnormal instances in unlabeled datasets [1], [9], [10]. Even though, in general, UL does not require labeled data, it still may require large datasets to effectively perform anomaly detection. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/621020583055360-1525074253850_Q64/Stephan-Pachnicke.jpg)
... Federated learning (FL) is a promising candidate to tackle the aforementioned issue by enabling the development of a global ML model using datasets owned by many parties without revealing their business-confidential data. In this respect, we presented a secure FL framework to predict the maintenance work for semiconductor lasers running in an optical network [4]. After the global ML model is deployed, each laser manufacturer benefits by receiving the personalized maintenance report on their hardware failure rate. ...
