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  2. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them. PyOD is an open-source Python library developed specifically for anomaly detection. [52] scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.

  3. Network behavior anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Network_Behavior_Anomaly...

    Network behavior anomaly detection (NBAD) is a security technique that provides network security threat detection. It is a complementary technology to systems that detect security threats based on packet signatures. [1] NBAD is the continuous monitoring of a network for unusual events or trends.

  4. Local outlier factor - Wikipedia

    en.wikipedia.org/wiki/Local_outlier_factor

    In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.

  5. Isolation forest - Wikipedia

    en.wikipedia.org/wiki/Isolation_forest

    Isolation Forest is an algorithm for data anomaly detection using binary trees.It was developed by Fei Tony Liu in 2008. [1] It has a linear time complexity and a low memory use, which works well for high-volume data.

  6. ELKI - Wikipedia

    en.wikipedia.org/wiki/ELKI

    Version 0.7.5 (February 2019) adds additional clustering algorithms, anomaly detection algorithms, evaluation measures, and indexing structures. [ 18 ] Version 0.8 (October 2022) adds automatic index creation, garbage collection, and incremental priority search, as well as many more algorithms such as BIRCH .

  7. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Each file represents a single experiment and contains a single anomaly. The dataset represents a multivariate time series collected from the sensors installed on the testbed. There are two markups for Outlier detection (point anomalies) and Changepoint detection (collective anomalies) problems 30+ files (v0.9) CSV Anomaly detection

  8. Anomaly-based intrusion detection system - Wikipedia

    en.wikipedia.org/wiki/Anomaly-based_intrusion...

    Another method is to define what normal usage of the system comprises using a strict mathematical model, and flag any deviation from this as an attack. This is known as strict anomaly detection. [3] Other techniques used to detect anomalies include data mining methods, grammar based methods, and Artificial Immune System. [2]

  9. Unsupervised learning - Wikipedia

    en.wikipedia.org/wiki/Unsupervised_learning

    Some of the most common algorithms used in unsupervised learning include: (1) Clustering, (2) Anomaly detection, (3) Approaches for learning latent variable models. Each approach uses several methods as follows: Clustering methods include: hierarchical clustering, [13] k-means, [14] mixture models, model-based clustering, DBSCAN, and OPTICS ...