Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies

no code implementations • 13 May 2024 • Mu-Huan Miles Chung, Sharon Li, Jaturong Kongmanee, Lu Wang, Yuhong Yang, Calvin Giang, Khilan Jerath, Abhay Raman, David Lie, Mark Chignell

We also recommend that the information gain maximizing sample method (based on expert confidence) should be used in early stages of Active Learning, providing that well-calibrated confidence can be obtained.

Active Learning

Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails

no code implementations • 1 Mar 2023 • Mu-Huan Chung, Lu Wang, Sharon Li, Yuhong Yang, Calvin Giang, Khilan Jerath, Abhay Raman, David Lie, Mark Chignell

In this paper we present research results concerning the application of Active Learning to anomaly detection in redacted emails, comparing the utility of different methods for implementing active learning in this context.

Active Learning Anomaly Detection