A comprehensive survey of anomaly detection algorithms
D Samariya, A Thakkar - Annals of Data Science, 2023 - Springer
Anomaly or outlier detection is consider as one of the vital application of data mining, which
deals with anomalies or outliers. Anomalies are considered as data points that are …
deals with anomalies or outliers. Anomalies are considered as data points that are …
A new effective and efficient measure for outlying aspect mining
Abstract Outlying Aspect Mining (OAM) aims to find the subspaces (aka aspects) in which a
given query is an outlier with respect to a given data set. Existing OAM algorithms use …
given query is an outlier with respect to a given data set. Existing OAM algorithms use …
usfAD: a robust anomaly detector based on unsupervised stochastic forest
In real-world applications, data can be represented using different units/scales. For example,
weight in kilograms or pounds and fuel-efficiency in km/l or l/100 km. One unit can be a …
weight in kilograms or pounds and fuel-efficiency in km/l or l/100 km. One unit can be a …
Identifying and clustering users for unsupervised intrusion detection in corporate audit sessions
M Garchery, M Granitzer - 2019 IEEE International Conference …, 2019 - ieeexplore.ieee.org
We address intrusion detection in audit sessions, focusing on masquerades and insider
threats. Unsupervised intrusion detection can straightforwardly be addressed through …
threats. Unsupervised intrusion detection can straightforwardly be addressed through …
Uncertainty quantification for machine learning output assurance using anomaly-based dataset dissimilarity measures
G Incorvaia, D Hond, H Asgari - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
The use of Machine Learning (ML) models as predictive tools has increased dramatically in
recent years. However, data-driven systems (such as ML models) exhibit a degree of …
recent years. However, data-driven systems (such as ML models) exhibit a degree of …
Factor analysis of mixed data for anomaly detection
M Davidow, DS Matteson - … Analysis and Data Mining: The ASA …, 2022 - Wiley Online Library
Anomaly detection aims to identify observations that deviate from the typical pattern of data.
Anomalous observations may correspond to financial fraud, health risks, or incorrectly …
Anomalous observations may correspond to financial fraud, health risks, or incorrectly …
Anomaly detection on health data
D Samariya, J Ma - International Conference on Health Information …, 2022 - Springer
The identification of anomalous records in medical data is an important problem with
numerous applications such as detecting anomalous reading, anomalous patient health …
numerous applications such as detecting anomalous reading, anomalous patient health …
Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures
G Incorvaia, D Hond, H Asgari - Electronics, 2024 - mdpi.com
The use of Machine Learning (ML) models as predictive tools has increased dramatically in
recent years. However, data-driven systems (such as ML models) exhibit a degree of …
recent years. However, data-driven systems (such as ML models) exhibit a degree of …
Ensemble of local decision trees for anomaly detection in mixed data
S Aryal, JR Wells - Machine Learning and Knowledge Discovery in …, 2021 - Springer
Anomaly Detection (AD) is used in many real-world applications such as cybersecurity,
banking, and national intelligence. Though many AD algorithms have been proposed in the …
banking, and national intelligence. Though many AD algorithms have been proposed in the …