Isolation-Based Anomaly Detection

By Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou. TKDD, 2012

1,970 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/dcd9f5d61bc9a70c40be84a8d78fbee822ffcd9e

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Citations in the corpus

Deep Anomaly Detection with Deviation Networks (2019, background): “noted as FSNet) [25], and iForest [14].”
Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices (2020, background): “Moreover, isolation methods work better even when some attributes are irrelevant to detect anomalies [54].”
Contextual Outlier Interpretation (2017, methodology): “Similar to the experiment in subsection 5.5, outlierness incurred by noise a ributes are undesired. e outlier detectors applied here include LOF [8], One-Class SVM [42] and Isolation Forest [29]. e three approaches are of di erent types, and involve disparate de nitions of outliers and algorithms to”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, methodology): “Let us note that some methods have been proposed to combine single outlier detectors into an ensemble [Lazarevic and Kumar 2005; Gao and Tan 2006; Nguyen et al. 2010; Kriegel et al. 2011a; Schubert et al. 2012; Liu et al. 2012; Zimek et al. 2013a, 2013, 2014].”
Extended Isolation Forest (2018, methodology): “The general algorithm for Isolation Forest [9], [11] starts”
Deep Learning for Anomaly Detection (2020, background): “t sophisticated anomalies are missed. Although a plethora of anomaly detection methods have been introduced over the years, the current state-ofthe-art methods, especially unsupervised methods (e.g., [17, 85]), still often incur high false positives on real-world datasets [20,116]. How to reduce fal”
Robust Random Cut Forest Based Anomaly Detection on Streams (2016, methodology): “For question (2), we explore a randomized approach, akin to (Liu et al., 2012), due in part to the practical success reported in (Emmott et al.”
k-fingerprinting: A Robust Scalable Website Fingerprinting Technique (2015, methodology): “Each tree in the forest is trained using labeled objects represented as feature vectors of a fixed size.”
Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives (2021, methodology): “Also, one-class random forest (OCRF) is proposed to identify abnormal consumption when labeled data are absent [44, 45], it is based on utilizing classifier ensemble randomization fundamentals [46].”