Interpretable Anomaly Detection with DIFFI: Depth-based feature importance of Isolation Forest
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Model-specific techniques such as References [7,32,95] generally have consistent Explanation-Definition and Detection-Definition of anomaly as they explore the internal structure of an anomaly detection model to generate explanations.
“Moreover, modelspecific techniques such as References [7,32,95] generally have consistent Explanation-Definition and Detection-Definition of anomaly as they explore the internal structure of an anomaly detection model to generate explanations.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [32]
The Isolation Forest unsupervised anomaly detection technique identified potentially malicious reports deviating from the expected distribution of valid reports.
“Utilized the unsupervised anomaly detection technique 'Isolation Forest' [27] to identify potentially malicious reports deviating from the expected distribution of valid reports.”
From Enhancing Intelligent Transport Systems Through Decentralized Security Frameworks in Vehicle-to-Everything Networks (2025) · cites this paper as [27]
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- Authors
- Carletti, M., Terzi, M., & Susto, G. A.
- Affiliations
- University of Padua (IT)
- Corresponding author
- Susto, Gian Antonio
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 119
- Pages
- 105730
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W3045149356
- Cited by
- 118
- References
- 64
- Citations by year
- 2026: 21 · 2025: 32 · 2024: 33 · 2023: 18 · 2022: 10 · 2021: 4
- FWCI
- 7.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- MIUR
- Università degli Studi di Padova
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- anomaly detection, Isolation Forest, interpretability, feature importance, multivariate anomaly detection, unsupervised feature selection
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