Data-Driven Anomaly Detection & Prediction for IoT

Anomaly detection/prediction usually relies on wide domain knowledge to build up the tools to automatically detect/predict abnormal events or behaviors of an IoT system. An IoT system may consist of machines with different capabilities, functionalities and ages. Abnormal events or behaviors are usually rare events. It is time-consuming and high-cost to build up the domain knowhow of the IoT systems and collect enough data points of the anomaly. In this lecture, I first identify the issues and challenges. Then I illustrate a general environment for anomaly detection/perdition. Then I will illustrate the technologies and solutions for anomaly detection/prediction, and show some prototypes and their applications.
Speaker(s): Phone Lin,
Virtual: https://events.vtools.ieee.org/m/358912