Market Overview:
The global deep learning in security market is expected to grow from USD 2.14 Billion in 2018 to USD 11.96 Billion by 2030, at a CAGR of 23.8% during the forecast period. The growth of the market can be attributed to the increasing adoption of deep learning in security applications, such as identity and access management, risk and compliance management, encryption, data loss prevention (DLP), unified threat management (UTM), antivirus/antimalware, intrusion detection/prevention systems (IDS/IPS), and others (firewall, DDoS protection,, disaster recovery). However, lack of skilled professionals is restraining the growth of the global deep learning in security market.
Product Definition:
Deep learning is a subset of machine learning that uses neural networks to learn how to recognize patterns in data. Neural networks are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data. Deep learning algorithms can be used to detect malicious activity and malware, identify intrusions and vulnerabilities, and defend against cyberattacks.
Hardware:
Hardware is the part of technology that enhances the performance of deep learning in data centers. It includes cooling systems, servers, storage devices and other communication products. The growth factor for hardware is high as compared to software due to its usage in large quantities for training and testing purposes.
Advancements in hardware such as increased memory size & speed, enhanced processing power along with easy access to cloud based services are expected to drive market growth over the forecast period.
Software:
Software is any collection of rules or method that can be used to get a certain result and perform a specific task. In computer science, software is an execution by which the user can mean either a) the part of programming code that gives instructions for carrying out an action or b) any set of instructions, often in the form of a program, which allows users to carry out tasks.
Application Insights:
The risk and compliance management application segment dominated the market in 2017. Deep learning helps organizations to manage, analyze, and prevent risks from different angles. It also enables them to continuously monitor business processes for potential vulnerabilities and threats. The technology can be effectively used across various applications such as fraud detection, supply chain management, network monitoring/segmentation, etc. Moreover, it provides real-time data for better decision-making which boosts the growth of this segment over the forecast period.
Regional Analysis:
The market in North America is expected to grow at a significant rate over the forecast period owing to increasing investments by governments and other organizations. The U.S. government has significantly contributed towards the growth of deep learning-based security systems through various funding programs, such as those under National Science Foundation (NSF) grants and Defense Advanced Research Projects Agency (DARPA). Moreover, growing adoption of AI technology across several sectors in countries such as Canada and Mexico are contributing towards regional growth. For instance, researchers from Montreal Institute for Learning Algorithms have developed an algorithm that can automatically detect if a face is in public or private space using DeepFace2 facial recognition software without any prior knowledge of context.
In Asia Pacific, vendors operating in this industry are likely to witness high demand from enterprises due to rising cybercrimes activities coupled with increased spending on internet infrastructure development by governments across economies including China.
Growth Factors:
- Increasing demand for deep learning in security from various end-user industries such as automotive, retail, and healthcare
- Growing number of cyber threats and data breaches across the globe
- Rising adoption of deep learning in security by small and medium businesses (SMBs)
- Proliferation of big data and emergence of IoT devices that are prone to cyber attacks
- Increasing focus on research and development (R&D) activities for developing new deep learning algorithms for security applications
Scope Of The Report
Report Attributes
Report Details
Report Title
Deep Learning in Security Market Research Report
By Type
Hardware, Software, Service
By Application
Identity and Access Management, Risk and Compliance Management, Encryption, Data Loss Prevention, Unified Threat Management, Antivirus/Antimalware, Intrusion Detection/Prevention Systems, Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery)
By Companies
NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), AWS (US), Graphcore (UK), Mythic (US), Adapteva (US), Koniku (US)
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
120
Number of Tables & Figures
84
Customization Available
Yes, the report can be customized as per your need.
Global Deep Learning in Security Market Report Segments:
The global Deep Learning in Security market is segmented on the basis of:
Types
Hardware, Software, Service
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Identity and Access Management, Risk and Compliance Management, Encryption, Data Loss Prevention, Unified Threat Management, Antivirus/Antimalware, Intrusion Detection/Prevention Systems, Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery)
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- NVIDIA (US)
- Intel (US)
- Xilinx (US)
- Samsung Electronics (South Korea)
- Micron Technology (US)
- Qualcomm (US)
- IBM (US)
- Google (US)
- Microsoft (US)
- AWS (US)
- Graphcore (UK)
- Mythic (US)
- Adapteva (US)
- Koniku (US)
Highlights of The Deep Learning in Security Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- Hardware
- Software
- Service
- By Application:
- Identity and Access Management
- Risk and Compliance Management
- Encryption
- Data Loss Prevention
- Unified Threat Management
- Antivirus/Antimalware
- Intrusion Detection/Prevention Systems
- Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery)
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Deep Learning in Security Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
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8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Deep learning is a subset of machine learning that uses artificial neural networks to learn patterns in data. Neural networks are modeled after the way the brain works, and they can be used to identify patterns in data that humans would not be able to see. This technology is being used more and more in security because it can help identify threats before they become serious problems.
Some of the key players operating in the deep learning in security market are NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), AWS (US), Graphcore (UK), Mythic (US), Adapteva (US), Koniku (US).
The deep learning in security market is expected to register a CAGR of 23.8%.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Deep Learning in Security Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Deep Learning in Security Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Deep Learning in Security Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Deep Learning in Security Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Deep Learning in Security Market Size & Forecast, 2020-2028
4.5.1 Deep Learning in Security Market Size and Y-o-Y Growth
4.5.2 Deep Learning in Security Market Absolute $ Opportunity
Chapter 5 Global Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Market Size Forecast by Type
5.2.1 Hardware
5.2.2 Software
5.2.3 Service
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Market Size Forecast by Applications
6.2.1 Identity and Access Management
6.2.2 Risk and Compliance Management
6.2.3 Encryption
6.2.4 Data Loss Prevention
6.2.5 Unified Threat Management
6.2.6 Antivirus/Antimalware
6.2.7 Intrusion Detection/Prevention Systems
6.2.8 Others (Firewall
6.2.9 Distributed Denial-of-Service (DDoS)
6.2.10 Disaster Recovery)
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Deep Learning in Security Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Deep Learning in Security Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Analysis and Forecast
9.1 Introduction
9.2 North America Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Market Size Forecast by Type
9.6.1 Hardware
9.6.2 Software
9.6.3 Service
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Market Size Forecast by Applications
9.10.1 Identity and Access Management
9.10.2 Risk and Compliance Management
9.10.3 Encryption
9.10.4 Data Loss Prevention
9.10.5 Unified Threat Management
9.10.6 Antivirus/Antimalware
9.10.7 Intrusion Detection/Prevention Systems
9.10.8 Others (Firewall
9.10.9 Distributed Denial-of-Service (DDoS)
9.10.10 Disaster Recovery)
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Analysis and Forecast
10.1 Introduction
10.2 Europe Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Market Size Forecast by Type
10.6.1 Hardware
10.6.2 Software
10.6.3 Service
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Market Size Forecast by Applications
10.10.1 Identity and Access Management
10.10.2 Risk and Compliance Management
10.10.3 Encryption
10.10.4 Data Loss Prevention
10.10.5 Unified Threat Management
10.10.6 Antivirus/Antimalware
10.10.7 Intrusion Detection/Prevention Systems
10.10.8 Others (Firewall
10.10.9 Distributed Denial-of-Service (DDoS)
10.10.10 Disaster Recovery)
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Market Size Forecast by Type
11.6.1 Hardware
11.6.2 Software
11.6.3 Service
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Market Size Forecast by Applications
11.10.1 Identity and Access Management
11.10.2 Risk and Compliance Management
11.10.3 Encryption
11.10.4 Data Loss Prevention
11.10.5 Unified Threat Management
11.10.6 Antivirus/Antimalware
11.10.7 Intrusion Detection/Prevention Systems
11.10.8 Others (Firewall
11.10.9 Distributed Denial-of-Service (DDoS)
11.10.10 Disaster Recovery)
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Analysis and Forecast
12.1 Introduction
12.2 Latin America Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Market Size Forecast by Type
12.6.1 Hardware
12.6.2 Software
12.6.3 Service
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Market Size Forecast by Applications
12.10.1 Identity and Access Management
12.10.2 Risk and Compliance Management
12.10.3 Encryption
12.10.4 Data Loss Prevention
12.10.5 Unified Threat Management
12.10.6 Antivirus/Antimalware
12.10.7 Intrusion Detection/Prevention Systems
12.10.8 Others (Firewall
12.10.9 Distributed Denial-of-Service (DDoS)
12.10.10 Disaster Recovery)
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Market Size Forecast by Type
13.6.1 Hardware
13.6.2 Software
13.6.3 Service
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Market Size Forecast by Applications
13.10.1 Identity and Access Management
13.10.2 Risk and Compliance Management
13.10.3 Encryption
13.10.4 Data Loss Prevention
13.10.5 Unified Threat Management
13.10.6 Antivirus/Antimalware
13.10.7 Intrusion Detection/Prevention Systems
13.10.8 Others (Firewall
13.10.9 Distributed Denial-of-Service (DDoS)
13.10.10 Disaster Recovery)
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Deep Learning in Security Market: Competitive Dashboard
14.2 Global Deep Learning in Security Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 NVIDIA (US)
14.3.2 Intel (US)
14.3.3 Xilinx (US)
14.3.4 Samsung Electronics (South Korea)
14.3.5 Micron Technology (US)
14.3.6 Qualcomm (US)
14.3.7 IBM (US)
14.3.8 Google (US)
14.3.9 Microsoft (US)
14.3.10 AWS (US)
14.3.11 Graphcore (UK)
14.3.12 Mythic (US)
14.3.13 Adapteva (US)
14.3.14 Koniku (US)
