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Anomaly Detection Market: Industry Size, Share, Growth, Forecasts 2017 - 2025

06-12-2018 08:20 AM CET | IT, New Media & Software

Press release from: MarketResearchReports.biz

Anomaly Detection Market: Industry Size, Share, Growth,

The report analyzes and presents an overview of "Anomaly Detection Market - Global Industry Analysis, Size, Share, Growth, Trends, and Forecast 2017 - 2025" worldwide.

The objectives of this study are as follows:

To define, describe, and forecast the "Anomaly Detection" market by type, application, component, delivery model, end user, and region
To provide detailed information regarding major factors influencing market growth (drivers, restraints, opportunities, and industry-specific challenges)
To analyze micromarkets with respect to individual growth trends, prospects, and contributions to the overall market
To analyze opportunities in the market for stakeholders and provide details of the competitive landscape for market leaders
To forecast the market size of market segments with respect to the four key regions: North America, Europe, Asia, and the Rest of the World (RoW)
To strategically profile the key players and comprehensively analyze their product portfolios, market positions, and core competencies

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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.

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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.

The report offers a comprehensive evaluation of the market. It does so via in-depth qualitative insights, historical data, and verifiable projections about market size. The projections featured in the report have been derived using proven research methodologies and assumptions. By doing so, the research report serves as a repository of analysis and information for every facet of the market, including but not limited to: Regional markets, technology, types, and applications.

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The study is a source of reliable data on:

Market segments and sub-segments
Market trends and dynamics
Supply and demand
Market size
Current trends/opportunities/challenges
Competitive landscape
Technological breakthroughs
Value chain and stakeholder analysis
The regional analysis covers:

North America (U.S. and Canada)
Latin America (Mexico, Brazil, Peru, Chile, and others)
Western Europe (Germany, U.K., France, Spain, Italy, Nordic countries, Belgium, Netherlands, and Luxembourg)
Eastern Europe (Poland and Russia)
Asia Pacific (China, India, Japan, ASEAN, Australia, and New Zealand)
Middle East and Africa (GCC, Southern Africa, and North Africa)
The report has been compiled through extensive primary research (through interviews, surveys, and observations of seasoned analysts) and secondary research (which entails reputable paid sources, trade journals, and industry body databases). The report also features a complete qualitative and quantitative assessment by analyzing data gathered from industry analysts and market participants across key points in the industry’s value chain.

A separate analysis of prevailing trends in the parent market, macro- and micro-economic indicators, and regulations and mandates is included under the purview of the study. By doing so, the report projects the attractiveness of each major segment over the forecast period.

Highlights of the report:

A complete backdrop analysis, which includes an assessment of the parent market
Important changes in market dynamics
Market segmentation up to the second or third level
Historical, current, and projected size of the market from the standpoint of both value and volume
Reporting and evaluation of recent industry developments
Market shares and strategies of key players
Emerging niche segments and regional markets
An objective assessment of the trajectory of the market
Recommendations to companies for strengthening their foothold in the market]

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