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  2. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    The concept of intrusion detection, a critical component of anomaly detection, has evolved significantly over time. Initially, it was a manual process where system administrators would monitor for unusual activities, such as a vacationing user's account being accessed or unexpected printer activity.

  3. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    RAWPED is a dataset for detection of pedestrians in the context of railways. The dataset is labeled box-wise. 26000 Images Object recognition and classification 2020 [70] [71] Tugce Toprak, Burak Belenlioglu, Burak Aydın, Cuneyt Guzelis, M. Alper Selver OSDaR23 OSDaR23 is a multi-sensory dataset for detection of objects in the context of railways.

  4. 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.

  5. Argus – Audit Record Generation and Utilization System

    en.wikipedia.org/wiki/Argus_–_Audit_Record...

    The audit trail has traditionally been used as historical network traffic measurement data for network forensics [5] and Network Behavior Anomaly Detection (NBAD). [6] Argus has been used extensively in cybersecurity, end-to-end performance analysis, software-defined networking (SDN) research, [7] and recently a very large number of AI/ML ...

  6. Isolation forest - Wikipedia

    en.wikipedia.org/wiki/Isolation_forest

    A higher number of trees improves anomaly detection accuracy but increases computational costs. The optimal number balances resource availability with performance needs. For example, a smaller dataset might require fewer trees to save on computation, while larger datasets benefit from additional trees to capture more complexity.

  7. 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.

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