A Study on Isolation Forest for Anomaly Detection in Cloud-Based Systems
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Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
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Sammanfattning
The need for effective monitoring solutions increases as more organizations migrate
to a cloud infrastructure. The leading cloud providers offer their own monitoring
services, but they lack customizability and are black-box, making it hard to understand
and control their behavior. This thesis investigates developing and deploying a
custom monitoring service to the cloud. This will be done by applying the Isolation
Forest (iForest) algorithm as an anomaly detection tool for monitoring cloud services
within Amazon Web Services (AWS). While iForest is a powerful unsupervised
learning algorithm, it is made for static analysis. Applying it to streaming data
introduces some challenges, such as concept drift and seasonal changes in the data.
Our research addresses these challenges by tailoring iForest to cloud monitoring.
As many of you are aware, more and more companies have been migrating their
operations to the cloud in order to enhance flexibility, scalability, and efficiency
in their operations. In this thesis, we studied iForest for detecting anomalies in
CloudWatch metrics data in the context of WirelessCar. The developed model
demonstrated superior performance compared to state-of-the-art alternatives. Additionally,
we deployed the model to the cloud through AWS where it was used
to detect anomalies in live data from a data stream. We have summarized the
findings and provided practical guidelines for anomaly detection development and
cloud deployment. These guidelines aim to assist practitioners and researchers with
integrating anomaly detection for cloud monitoring.
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Ämne/nyckelord
software, engineering, anomaly, detection, isolation, forest, streaming, guidelines, AWS