anomaly detection


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anomaly detection

[ə′näm·ə·lē di‚tek·shən]
(computer science)
The technology that seeks to identify an attack on a computer system by looking for behavior that is out of the norm.

anomaly detection

(1) An approach to intrusion detection that establishes a baseline model of behavior for users and components in a computer system or network. Deviations from the baseline cause alerts that direct the attention of human operators to the anomalies. See IDS and anomaly.

(2) Detecting data that lie outside the normal range. Also called "outlier detection."
References in periodicals archive ?
Correia, "TAT-NIDS: an immune-based anomaly detection architecture for network intrusion detection," in Proceedings of the 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB '08), pp.
Here we summarize the main procedure for solving our network anomaly detection problem by APG algorithm, which is called NAD-APG (see Algorithm 1).
Anomaly Detection 3.0 can be downloaded from http://anomalydetection.info [24].
Therefore, the anomaly detection proposed in this study first identifies a typical demand profile for each building to capture its own unique characteristics, then the actual demand is compared to the typical profile and the deviations from this typical profile are quantified.
So, the fact that one of the hot focuses of data science is anomaly detection should come as no surprise.
In Section 3, we introduce our anomaly detection approach in detail and present the system design.
In April 2017, TEPCO FP and MHPS installed their jointly developed anomaly detection model at the Pagbilao Power Station.
Behavior-based anomaly detection is foundational to any ICS cybersecurity approach.
Adaptive anomaly detection scheme for cloud computing based on LOF is presented by T.
Compared with hand-crafted feature-based anomaly detection approaches that depend on predefined heuristics, deep learning-based anomaly detection is easy to realize and generalize to different surveillance scenes.
Furthermore, Zhou S and Xu W have constructed the local anomaly detection algorithm based on the deviation in [7].