To address this, researchers have developed multiclass anomaly detection algorithms that combine NSA with clonal selection algorithms (CSA) to improve classification accuracy and reduce runtime in ...

AI-based anomaly detection helps engineers identify potential problems early, to improve process efficiency, says Rachel ...

Anomaly detection algorithms are leading the charge to take organizations away from the limitations of manually monitoring datasets. In its place is a wave of solutions that can not only make use of ...

Renmin University researchers present a comprehensive survey of sampling- and sketch-based algorithms for distinct value estimation in big data, ...

The problem of anomaly detection is not new, and a number of solutions have already been proposed over the years. However, before starting with the list of techniques, let's agree on a necessary ...

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years.

Numenta Anomaly Benchmark results for a machine temperature data file with two known anomalies. Algorithms (represented by shapes) that make detections within the pink anomaly windows receive ...

Although predicated on advanced math concepts, anomaly detection, or as the NIST Cybersecurity Framework 2.0 calls it, “adverse event analysis,” has over the past two decades been incorporated ...

Artificial Immune Systems (AIS) and anomaly detection algorithms are computational methods inspired by the adaptive and self-regulating properties of the biological immune system.