  Anomaly detection is critical in various fields, including intrusion
detection, health monitoring, fault diagnosis, and sensor network event
detection. The isolation forest (or iForest) approach is a well-known technique
for detecting anomalies. It is, however, ineffective when dealing with dynamic
streaming data, which is becoming increasingly prevalent in a wide variety of
application areas these days. In this work, we extend our previous work by
proposed an efficient iForest based approach for anomaly detection using cube
sampling that is effective on streaming data. Cube sampling is used in the
initial stage to choose nearly balanced samples, significantly reducing storage
requirements while preserving efficiency. Following that, the streaming nature
of data is addressed by a sliding window technique that generates consecutive
chunks of data for systematic processing. The novelty of this paper is in
applying Cube sampling in iForest and calculating inclusion probability. The
proposed approach is equally successful at detecting anomalies as existing
state-of-the-art approaches, requiring significantly less storage and time
complexity. We undertake empirical evaluations of the proposed approach using
standard datasets and demonstrate that it outperforms traditional approaches in
terms of Area Under the ROC Curve (AUC-ROC) and can handle high-dimensional
streaming data.
