Western-OC2-Lab/OASW-Concept-Drift-Detection-and-Adaptation
An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.
This project helps operations engineers and security analysts maintain accurate anomaly detection in IoT systems. It takes in live streams of IoT sensor data, such as network traffic, and automatically adjusts its predictive models. The output is a continuously updated anomaly detection system that remains effective even as data patterns change over time.
No commits in the last 6 months.
Use this if you need to detect unusual activity or potential security threats in real-time IoT data streams where patterns frequently evolve.
Not ideal if your data patterns are static and predictable, or if you need to perform batch analysis rather than continuous online learning.
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MIT
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Last pushed
Jan 20, 2024
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