市场调查报告书
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1335857
全球异常侦测的全球市场规模、份额和行业趋势分析报告:按部署、按技术、按组件(解决方案(网路行为、用户行为)、服务)、按最终用户、按地区、前景和预测:2023-2030Global Anomaly Detection Market Size, Share & Industry Trends Analysis Report By Deployment, By Technology, By Component (Solution (Network Behavior, and User Behavior), and Services), By End-Use, By Regional Outlook and Forecast, 2023 - 2030 |
到 2030 年,异常侦测市场规模预计将达到 134 亿美元,预测期内市场年复合成长率率为 15.9%。
根据 KBV Cardinal 矩阵中的分析,微软公司是该市场的领导者。 Cisco Systems, Inc.、Broadcom, Inc. 和 Dell Technologies, Inc. 等公司是该市场的主要创新者。 2022 年 3 月,思科系统公司将与 NetApp 合作,为两家公司的客户提供自动化、混合云营运和视觉性解决方案。
市场成长要素
资料量和连接设备的增加
随着银行、IT、医疗保健、金融、製造、政府和国防等领域连接设备数量的增加,对异常侦测的需求也不断增加。积极参与各种技术进步的物联网解决方案的普及,对物联网产业产生了巨大影响。由于云端基础的物联网设备的使用不断增加,以及为各种最终用途行业提供最佳解决方案的竞争日益激烈,该市场正在快速成长。此外,物联网产业庞大发展的主要原因之一是各国政府正在大力尝试将企业和部门数位。
人工智慧 (AI) 和机器学习 (ML) 的进步
人工智慧和机器学习技术的进步显着提高了我们侦测异常的能力。当人力资源不足以处理云端基础设施、微服务和容器等适应性框架时,可以使用人工智慧(AI),例如自动化、即时分析、谨慎性、准确性和自学习,可以帮助很多方法。人工智慧系统和基于机器学习的解决方案的最大好处之一是它们能够边学习边学习,并在每次迭代中提供更好、更准确的结果。因此,人工智慧驱动的异常侦测工具可以评估复杂的模式,适应不断变化的环境,并精确地找出异常,从而推动市场扩张。
市场抑制因素
错误讯息和系统实施问题
异常侦测系统可能难以建构和调整以识别真正的异常,同时避免侦测(或误报)。高侦测率会降低使用者对系统准确性的信心,导致警告疲劳,并阻碍产品普及。误报率太高可能会导致警告疲劳和对系统缺乏信心,而误报率太低可能会导致重大异常被忽视。为了扩大市场,必须提高异常侦测演算法的准确性。将异常侦测工具整合到目前的工作流程和系统中可能既困难又耗时。面临与遗留系统的相容性问题的组织可能会延迟异常侦测技术的采用。因此,这些要素可能会阻碍未来几年的市场成长。
发展前景
根据实施型态,市场分为云端类型和本地类型。 2022 年,云端细分市场在市场中占据了重要的收入份额。云端基础的异常侦测系统具有出色的适应性和扩充性。透过利用云端基础设施,企业可以根据自己的需求轻鬆扩展或缩减异常侦测功能。资料处理和资料量需求随着时间的推移而变化,因此透过利用云端基础设施,企业不必在基础设施或容量规划上花费大量资金。
技术展望
依技术划分,市场分为机器学习和人工智慧、巨量资料分析、商业智慧和资料探勘。巨量资料分析领域在 2022 年创下了最大的市场销售份额。随着互联设备和数数位技术的进步,企业正在从多个来源产生和收集大量资料。这些资料以非结构化、结构化和半结构化格式提供,因此很难手动侦测诈欺。
组件展望
根据组件,市场分为解决方案和服务。 2022年的市场成长率高度依赖服务领域。云端基础的安全服务解决方案通常包括异常侦测服务。这些服务使公司能够轻鬆且廉价地设定和维护异常侦测操作。
解决方案展望
解决方案分为网路行为和使用者行为。 2022年,在同一市场中,网路行为领域将占据最大的收入份额。网路行为异常侦测需要网路行为分析。机器学习(ML)和人工智慧(AI)用于网路行为异常侦测,以识别网路基础设施中其他安全技术无法存取的区域中的隐患,并向网路负责人。
最终用途展望
依最终用户划分,可分为 BFSI、零售、IT/通讯、医疗保健、製造、政府/国防等。 BFSI 细分市场在 2022 年获得了最大的市场收入份额。风险管理对于 BFSI 产业极为重要。异常侦测可协助您识别市场风险、操作风险、信用风险、诈骗风险等潜在风险。透过识别金融交易、客户行为和市场模式中的异常情况,组织可以评估和最小化风险,做出明智的决策并防止财务损失。
区域展望
从区域来看,我们对北美、欧洲、亚太地区和拉丁美洲地区的市场进行了分析。 2022 年,北美市场收入份额最高。北美大陆面临着快速变化和不稳定的环境,特别是在网路安全方面。数位科技的普及以及巨量资料的发展也导致企业产生和收集大量资料。异常侦测对于发现保险、电子商务、金融和领域领域的诈欺至关重要。透过监控交易资料和使用者行为的模式和异常情况,公司可以主动识别诈欺并降低风险。
The Global Anomaly Detection Market size is expected to reach $13.4 billion by 2030, rising at a market growth of 15.9% CAGR during the forecast period.
The digital economy has swiftly grown throughout the region of Asia Pacific as a result of a strong increase in e-commerce activity, online transactions, and digital services. Consequently, the Asia Pacific region will acquire approximately 1/4th share in the market by 2030. The need for anomaly detection has grown due to this expansion to identify and handle potential fraud, security flaws, and other anomalies in these digital transactions. The regional financial services sector is expanding rapidly because of growing banking services, fintech advancements, and a rise in digital payments. Anomaly detection is crucial for Anti-Money Laundering (AML) initiatives, fraud prevention, and legal compliance in this sector.
The major strategies followed by the market participants are Partnerships as the key developmental strategy to keep pace with the changing demands of end users. For instance, In June, 20223, Amazon Web Services Inc. expanded its partnership with Lacework Inc. to enhance security alerts and provide its clients an improved anomaly detection linked with Amazon Guard Duty findings. Additionally, In December, 2021, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company formed a collaboration with Pfizer, to develop a prototype solution for detecting abnormal data points in its drug product continuous clinical manufacturing platform for solid oral-dose medicines.
Based on the Analysis presented in the KBV Cardinal matrix; Microsoft Corporation is the forerunner in the Market. Companies such as Cisco Systems, Inc., Broadcom, Inc., Dell Technologies, Inc. are some of the key innovators in the Market. In March, 2022, Cisco Systems, Inc teamed up with NetApp to provide the joint customers of the two companies with automation, hybrid cloud operations, and visibility solutions.
Market Growth Factors
Increasing volume of data and connected devices
Anomaly detection is becoming increasingly necessary as the number of linked devices is increasing in banking, IT, healthcare, finance, manufacturing, and government & defense. The widespread use of IoT solutions that actively participate in various technological advancements significantly impacts the IoT industry. The market has seen an upsurge due to the increasing use of cloud-based IoT devices, which has increased competition to provide the best solutions to various end-use industries. Moreover, one of the main causes of the IoT industry's enormous development is considerable government attempts to digitalize businesses and sectors.
Artificial intelligence (AI) and machine learning (ML) advancements
The ability to detect anomalies has substantially increased because of developments in AI and machine learning techniques. Artificial intelligence (AI) may aid in many ways, including automation, real-time analysis, scrupulosity, accuracy, and self-learning, when human resources are insufficient to handle the adaptable framework of cloud infrastructure, microservices, and containers. One of the greatest benefits of AI systems as well as ML-based solutions, is their ability to learn as they go along and provide better and more accurate results with each iteration. Hence, AI-powered anomaly detection tools can evaluate complicated patterns, adapt to shifting surroundings, and accurately pinpoint anomalies, spurring market expansion.
Market Restraining Factors
Issues with false alarms and system implementation
Anomaly detection systems can be challenging to build and tune to identify true anomalies while avoiding false positives (or false alarms). High rates of false positives could reduce user confidence in the system's accuracy and lead to warning fatigue, which could prevent product uptake. False positive rates that are too high can cause alert fatigue and a lack of faith in the system, whereas false negative rates that are too low can leave serious anomalies unnoticed. For the market to expand, anomaly detection algorithms' accuracy must be improved. Integrating anomaly detection tools with current workflows and systems can be difficult and time-consuming. Implementing anomaly detection technology may be slowed down by organizations facing compatibility problems with legacy systems. Thus, these factors may hamper the market's growth in the coming years.
Deployment Outlook
Based on deployment, the market is segmented into cloud and on-premise. The cloud segment acquired a substantial revenue share in the market in 2022. Cloud-based anomaly detection systems are unsurpassed in their adaptability and scalability. Organizations may easily scale up or down anomaly detection capabilities in accordance with their needs because of cloud infrastructure. Because data processing and volume requirements fluctuate over time, organizations don't need to spend much money on infrastructure or plan for capacity with cloud infrastructure.
Technology Outlook
On the basis of technology, the market is classified into machine learning & artificial intelligence, big data analytics, and business intelligence & data mining. The big data analytics segment recorded the largest revenue share in the market in 2022. As connected devices and digital technology advance, businesses produce and collect large amounts of data from multiple sources. Manually finding irregularities can be challenging because this data is available in both unstructured, structured, and semi-structured, formats.
Component Outlook
Based on component, the market is bifurcated into solution and services. The services segment procured a considerable growth rate in the market in 2022. Cloud-based security service solutions commonly incorporate anomaly detection services. With the help of these services, enterprises can easily and affordably set up as well as maintain anomaly detection operations.
Solution Outlook
On the basis of the solution, the market is classified into network behavior and user behavior. The network behavior segment acquired the largest revenue share in the market in 2022. Network behavior analysis is required for the operation of network behavior anomaly detection. Machine learning (ML) and artificial intelligence (AI) are used in network behavior anomaly detection to identify hidden hazards in areas of network infrastructure where other security technologies cannot access them and to alert network personnel.
End-Use Outlook
By end-use, the market is characterized into BFSI, retail, IT & telecom, healthcare, manufacturing, government & defense, and others. The BFSI segment garnered the maximum revenue share in the market in 2022. Risk management is crucial to the BFSI industry. Anomaly detection makes it possible to identify potential risks, including market risk, operational risk, credit risk, and fraud risk. By identifying anomalies in financial transactions, customer behavior, or market patterns, organizations can assess and minimize risks, make intelligent decisions, and prevent financial losses.
Regional Outlook
Region wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America segment recorded the highest revenue share in the market in 2022. The continent of North America is subject to an unstable environment that is changing quickly, especially regarding cybersecurity. The proliferation of digital technology, along with the development of big data, has also led to huge data production and collection by companies. Anomaly detection is essential for spotting fraudulent activities in the insurance, e-commerce, financial, and healthcare sectors. By monitoring patterns and anomalies in transactional data or user behavior, businesses can proactively identify and lower the risk of fraud.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Amazon Web Services, Inc., Broadcom, Inc., Cisco Systems, Inc., Dell Technologies, Inc., Dynatrace, Inc., Happiest Minds Technologies Limited, Hewlett Packard Enterprise Company, IBM Corporation, Microsoft Corporation and SAS Institute, Inc.
Recent Strategies Deployed in Anomaly Detection Market
Partnerships, Collaboration and Agreements:
Jun-2023: Amazon Web Services Inc. expanded its partnership with Lacework Inc., a cloud security company. Lacework would integrate its services with AWS Security Hub to enhance security alerts and provide its clients an improved anomaly detection linked with Amazon GuardDuty findings.
May-2023: Amazon Web Services joined hands with Elastic, distributed, free, and open search and analytics engine for all types of data. The collaboration aims at offering a seamless user experience for Elastic Cloud on AWS. Moreover, it would support its client's global cloud adoption journey and help boost their digital transformation.
Nov-2022: Happiest Minds Technologies Limited formed a collaboration with ServiceNow, a software company that provides a cloud-based platform for automating IT management workflows. With this collaboration, the company aims to enhance its IT service offerings globally.
May-2022: IBM Corporation signed an agreement with Amazon Web Services (AWS), a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments. This agreement would deliver IBM's clients easy and rapid access to IBM Software that covers Data and AI, Security, Sustainability, and Automation abilities.
Mar-2022: Cisco Systems, Inc teamed up with NetApp, a data management solutions provider. The partnership would provide the joint customers of the two companies with automation, hybrid cloud operations, and visibility solutions.
Dec-2021: Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company formed a collaboration with Pfizer, an American multinational pharmaceutical and biotechnology corporation. The company would apply its analytics, machine learning, computing, storage, security, and cloud data warehousing capabilities to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. Furthermore, the company aimed to develop a prototype solution for detecting abnormal data points in its drug product continuous clinical manufacturing platform for solid oral-dose medicines.
Aug-2021: IBM teamed up with Black & Veatch, an engineering, procurement, consulting, and construction company. The collaboration integrates Black & Veatch Asset Management Services (AMS) and digital analytics with IBM Maximo Application Suite to enhance the performance of assets and extend their lifespans.
Product Launch and Product Expansions:
Mar-2021: Amazon Web Services revealed Amazon Lookout for Metrics, an anomaly detection service, to monitor the health of its client's businesses. The new service aims at opening machine learning technology to more manufacturing plants by removing barriers involved in developing, training, deploying, monitoring, and fine-tuning computer vision models.
Acquisitions and Merger:
Mar-2023: Cisco Systems, Inc completed the acquisition of Lightspin Technologies Ltd., a security software provider based in Israel. The acquisition would enhance Cisco's ability to deliver secure solutions for cloud environments to their customers.
Jul-2022: IBM took over Databand.ai, a leading provider of data observability software. This acquisition aimed to provide IBM with the most comprehensive set of observability offerings for IT across applications, data, and machine learning and would continue to provide IBM's customers and partners with the technology they require to provide trustworthy data and AI at scale.
Mar-2022: Microsoft took over Nuance Communications, a leader in conversational AI and ambient intelligence industries. This acquisition aimed to bring together Nuance's best-in-class conversational AI and ambient intelligence with Microsoft's secure as well as trusted industry cloud offerings. Also, this acquisition would help providers offer more affordable, effective, and accessible healthcare, and help businesses in every industry create more personalized and meaningful customer experiences.
Market Segments covered in the Report:
By Deployment
By Technology
By Component
By End-Use
By Geography
Companies Profiled
Unique Offerings from KBV Research
List of Figures