Anomaly Detection

Terms Glossary A Anomaly Detection

Edit This Term Record

	Anomaly Detection

Anomaly Detection

Formal	A statistical technique that determines what patterns are normal and then identifies items that do not conform to those patterns. It is applicable in intrusion detection, fraud detection, system health monitoring, event detection in sensor networks, and detecting eco-system disturbances. Read More
Practical	Unlike simple classification where the classes are known in advance, in anomaly detection the users don’t know what they are looking for in the data. Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting Eco-system disturbances. It is often used in preprocessing to remove anomalous data from the dataset. In supervised learning, removing the anomalous data from the dataset often results in a statistically significant increase in accuracy. Read More
Contents

Formal

A statistical technique that determines what patterns are normal and then identifies items that do not conform to those patterns. It is applicable in intrusion detection, fraud detection, system health monitoring, event detection in sensor networks, and detecting eco-system disturbances.

Practical

Unlike simple classification where the classes are known in advance, in anomaly detection the users don’t know what they are looking for in the data. Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting Eco-system disturbances. It is often used in preprocessing to remove anomalous data from the dataset. In supervised learning, removing the anomalous data from the dataset often results in a statistically significant increase in accuracy.

Hewlett Packard Enterprise (HPE)

Hewlett Packard Enterprise or HPE (formerly HP) makes IT environments more efficient, productive and secure, enabling fast, flexible responses to a rapidly changing competitive landscape. They enable organizations to act quickly on ideas by delivering infrastructure that can be easily composed and recomposed to meet shifting demands, so they can lead in today’s marketplace of disruptive innovation. Year founded: 2015 (1939) Revenue: $53.0 billion (2014) NYSE: HPE

Overview

Anomaly Detection

Related Suppliers