Anomaly detection is a data mining process used to determine types of anomalies found in a data set and to determine details about their occurrences. Anomaly detection is also known as outlier detection. This process is used in domains for intrusion detection, fraud detection, system health monitoring, fault detection, and event detection systems in sensor networks. Anomaly detection helps users to alert about suspicious activity by third party, and also block further login attempts.
Increasing number of connected devices, and increasing fraudulent activities and cyber-attacks are key factors driving growth of the global anomaly detection market. In addition, development of high performance data analysis (HPDA), increasing internal threats among enterprises, and rising adoption of anomaly detection solutions in software testing are other major factors expected to fuel growth of the global anomaly detection market over the forecast period.
Also growing trend of Internet of Things (IoT), Bring Your Own Device (BYOD), and Industrial Internet of Everything (IIoE) giving rise to more security threats and cyber-attacks is expected to drive demand for adoption of anomaly detection solution.
However, high cost and strong competition from open-source alternatives in the market are key factors restraining growth of the global anomaly detection market. Additionally, lack of professional workforce to operate tools and solutions, and occurrence of asymmetric faults in open-source model are other factors expected to hinder growth of the global anomaly detection market over the forecast period.
The global anomaly detection market is segmented on the basis of solution, service, technology, deployment, application, and region. On the basis of region, the global anomaly detection market is segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.
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