Hsin Chen

Data Scientist

Hsin Chen is a data scientist at Vectra, where she researches security threats, runs analysis on big data, and trains machine learning models to detect cyberattacks. She implements various machine learning techniques, such as neural networks and unsupervised learning for anomaly detection. Hsin earned her master’s degree in mechanical engineering from Stanford University.

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Threat detection

Improving threat-hunting efficiency with the multi-homed attribute

July 9, 2019
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Hsin Chen

Data Scientist

Hsin Chen is a data scientist at Vectra, where she researches security threats, runs analysis on big data, and trains machine learning models to detect cyberattacks. She implements various machine learning techniques, such as neural networks and unsupervised learning for anomaly detection. Hsin earned her master’s degree in mechanical engineering from Stanford University.

Posts from

Hsin Chen

July 9, 2019
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By
Hsin Chen
Improving Threat-hunting Efficiency with the Multi-homed Attribute

In a previous blog, we spoke about the importance of security enrichments in your network metadata. These serve as the foundation for threat hunters and analysts to test and query against hypotheses during an investigative process.

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