Anomaly detection refers to the detection of abnormal or unusual behavior. It is a technique used to identify unusual patterns that do not perform according to ordinary behavior, called outliers. Anomalies can be point anomalies, context anomalies or collective anomalies. Anomaly Detection is generally done with the practice code and proprietary tools and solution. Developers are using anomaly detection systems to monitor the functionality of the software during software testing stage of software development lifecycle (SDLC). Developer utilizes detection tools to instantly remove irregularities during program executions and constantly check behavior of program against these abnormalities, and to assess and report all the detected anomalies.

Anomalies are basically outliers, noise, deviations or exceptions which is disrupting the normal operations of device on network. Anomaly detection is applicable in a various domains, such as detection of faults, health monitoring systems to detect irregularities, detection of events in sensor network, and detecting disturbances related to ecosystems. Detection tools are frequently used in preprocessing to eliminate inconsistent data from the datasets. In supervised learning of networks, eliminating the irregular data from the dataset frequently results in substantial increase in precision. Detection of anomalies will be used in data mining turning data into valuable business insights and optimization of business processes.

High performance data analytics (HPDA) allows data experts to detect strange behavior of device in real-time and helps in monitoring device performance, along with protecting data against unauthorized access. A network behavior abnormality is an abrupt and transitory deviation from the normal operation of the network. Some anomalies present on the network are intentionally caused by invaders with malicious intents, such as a denial of service (DoS) attack, while others may be unintentional. Rapid detection of anomalies is required to initiate a timely response within network and to enhance the performance and reduce operating overheads. As the number of devices in network are increasing, the associated risk of protecting the data is also increasing, due to which the anomaly detection market is growing at a substantial pace.

Factors such as growth of big data and rise of high performance data analytics (HPDA), increase demand of anomaly detection solutions in software testing, growth of cyber surveillance and fraudulent activities, need for security intelligence for protection against intrusion and increase in internal threats among enterprises are expected to drive the market during the forecast period.

Anomaly detection tools and solutions are used for analysis of network behavior and user behavior. Intense competition from open-source alternatives is expected to hinder the demand for commercial solutions. Lack of expertise and skills to operate the tools and solutions is major challenge. All such factors are expected to restrain the market growth during the forecast period.

Market for anomaly detection can be segmented on the basis of solution, deployment, service, technology, industry and geography. On the basis of geography, the market is segmented into North America, Europe, Asia Pacific (APAC), South America and Middle East & Africa (MEA). On the basis of solution, the market is segmented into user behavior anomaly detection and network behavior anomaly detection.  On the basis of deployment, the market is segmented into cloud, on-premise, and hybrid. The market is segmented by industry into IT and telecommunication, banking, financial services and insurance, retail, manufacturing, defense, healthcare and others. On the basis of service, the market can be classified into professional services and managed services. On the basis of technology, the market is segmented into big data analytics, data mining and business intelligence, machine learning and artificial intelligence.

Key players in anomaly detection market are Anodot Ltd., Cisco Systems Inc., Cynet, Dell Technologies Inc., Flowmon Networks, GreyCortex s.r.o., Guardian Analytics, Gurucul, Happiest Minds, International Business Machines Corporation, LogRhythm Inc., Niara Inc., Rapid7 Inc., SAS Institute Inc., Securonix Inc., Splunk Inc., Symantec, Corporation, Trend Micro Inc., Trustwave Holdings Inc., Aqueduct Technologies Inc, Varonis Systems Inc. Hewlett Packard Enterprise Company and Wipro Limited.

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