방법 비교

	온라인 오토인코더 이상 탐지	Isolation Forest
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2010s–present	2008
창시자	Various (online/incremental deep learning community)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
유형	Online unsupervised anomaly detection	Unsupervised ensemble (random partitioning trees)
원전	An, J. & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center, 2015-2.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
별칭	incremental autoencoder anomaly detection, streaming autoencoder anomaly detection, online AE anomaly detection, continual autoencoder anomaly detection	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
관련	5	5
요약	Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learning, making it suitable for real-time or high-volume streaming scenarios where batch retraining is impractical.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.