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Home > Deep Learning in Security - Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030

Deep Learning in Security - Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030

Publication ID: 
PB510242293
Publication Date: 
October 1, 2024
Pages: 
108
Publisher: 
MRRPB5
Countries: 
Global [1]
USD 3,950.00

Please choose the suitable license type from above. More details are given under tab "Report License Types" below.

IT Services [2]
Computing & Electronics [3]
Description: 

Deep learning algorithms are capable of detecting more advanced threats and are not reliant on remembering known signatures and common attack patterns. Instead, they learn the system and can recognize suspicious activities that might indicate the presence of bad actors or malware
The global market for Deep Learning in Security was estimated to be worth US$ million in 2023 and is forecast to a readjusted size of US$ million by 2030 with a CAGR of % during the forecast period 2024-2030.
Deep Learning is a subfield of machine learning that focuses on training artificial neural networks to mimic human cognitive processes, such as visual, auditory, and linguistic understanding. It has seen significant growth and adoption across various industries, including search technology, data mining, machine translation, natural language processing, multimedia learning, speech recognition, and recommendation systems.

The Deep Learning market is expected to grow at a rapid pace in the coming years, driven by increasing demand for advanced analytics, growing adoption of AI in various industries, and improvements in computational capabilities.

Some trends driving the Deep Learning market include:

1. The increasing availability of large datasets: As the volume, variety, and complexity of data continue to grow, Deep Learning algorithms can leverage these datasets to improve their performance and accuracy.

2. Adoption in industries: Deep Learning is being widely adopted in industries such as healthcare, finance, retail, automotive, and cybersecurity to automate tasks, improve efficiency, and make data-driven decisions.

3. Enhanced automation and robotics: Deep Learning is enabling the development of advanced robots and autonomous systems that can perform tasks requiring human-like intelligence.

4. Integration with other AI technologies: Deep Learning is increasingly being combined with other AI technologies such as machine learning, computer vision, and natural language processing to create more sophisticated applications.

5. Progress in hardware and infrastructure: Advances in hardware and infrastructure, such as GPUs and cloud computing, have made it possible to deploy and scale Deep Learning models more efficiently and cost-effectively.

6. Ongoing research and development: The Deep Learning field is constantly evolving, with researchers and developers working on improving algorithms, models, and applications.

Overall, the Deep Learning market is expected to continue its strong growth trajectory in the coming years, as more and more industries adopt these advanced technologies to drive innovation and improve operations.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Deep Learning in Security, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Deep Learning in Security by region & country, by Type, and by Application.
The Deep Learning in Security market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Deep Learning in Security.
Market Segmentation
By Company
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)
Segment by Type:
Hardware
Software
Service
Segment 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 Region
North America
United States
Canada
Europe
Germany
France
U.K.
Italy
Russia
Asia-Pacific
China
Japan
South Korea
China Taiwan
Southeast Asia
India
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
Turkey
Saudi Arabia
UAE
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Deep Learning in Security manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Deep Learning in Security in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Deep Learning in Security in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.

Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.

Table Of Contents: 

1 Market Overview
1.1 Deep Learning in Security Product Introduction
1.2 Global Deep Learning in Security Market Size Forecast
1.3 Deep Learning in Security Market Trends & Drivers
1.3.1 Deep Learning in Security Industry Trends
1.3.2 Deep Learning in Security Market Drivers & Opportunity
1.3.3 Deep Learning in Security Market Challenges
1.3.4 Deep Learning in Security Market Restraints
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Competitive Analysis by Company
2.1 Global Deep Learning in Security Players Revenue Ranking (2023)
2.2 Global Deep Learning in Security Revenue by Company (2019-2024)
2.3 Key Companies Deep Learning in Security Manufacturing Base Distribution and Headquarters
2.4 Key Companies Deep Learning in Security Product Offered
2.5 Key Companies Time to Begin Mass Production of Deep Learning in Security
2.6 Deep Learning in Security Market Competitive Analysis
2.6.1 Deep Learning in Security Market Concentration Rate (2019-2024)
2.6.2 Global 5 and 10 Largest Companies by Deep Learning in Security Revenue in 2023
2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Deep Learning in Security as of 2023)
2.7 Mergers & Acquisitions, Expansion
3 Segmentation by Type
3.1 Introduction by Type
3.1.1 Hardware
3.1.2 Software
3.1.3 Service
3.2 Global Deep Learning in Security Sales Value by Type
3.2.1 Global Deep Learning in Security Sales Value by Type (2019 VS 2023 VS 2030)
3.2.2 Global Deep Learning in Security Sales Value, by Type (2019-2030)
3.2.3 Global Deep Learning in Security Sales Value, by Type (%) (2019-2030)
4 Segmentation by Application
4.1 Introduction by Application
4.1.1 Identity and Access Management
4.1.2 Risk and Compliance Management
4.1.3 Encryption
4.1.4 Data Loss Prevention
4.1.5 Unified Threat Management
4.1.6 Antivirus/Antimalware
4.1.7 Intrusion Detection/Prevention Systems
4.1.8 Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery)
4.2 Global Deep Learning in Security Sales Value by Application
4.2.1 Global Deep Learning in Security Sales Value by Application (2019 VS 2023 VS 2030)
4.2.2 Global Deep Learning in Security Sales Value, by Application (2019-2030)
4.2.3 Global Deep Learning in Security Sales Value, by Application (%) (2019-2030)
5 Segmentation by Region
5.1 Global Deep Learning in Security Sales Value by Region
5.1.1 Global Deep Learning in Security Sales Value by Region: 2019 VS 2023 VS 2030
5.1.2 Global Deep Learning in Security Sales Value by Region (2019-2024)
5.1.3 Global Deep Learning in Security Sales Value by Region (2025-2030)
5.1.4 Global Deep Learning in Security Sales Value by Region (%), (2019-2030)
5.2 North America
5.2.1 North America Deep Learning in Security Sales Value, 2019-2030
5.2.2 North America Deep Learning in Security Sales Value by Country (%), 2023 VS 2030
5.3 Europe
5.3.1 Europe Deep Learning in Security Sales Value, 2019-2030
5.3.2 Europe Deep Learning in Security Sales Value by Country (%), 2023 VS 2030
5.4 Asia Pacific
5.4.1 Asia Pacific Deep Learning in Security Sales Value, 2019-2030
5.4.2 Asia Pacific Deep Learning in Security Sales Value by Country (%), 2023 VS 2030
5.5 South America
5.5.1 South America Deep Learning in Security Sales Value, 2019-2030
5.5.2 South America Deep Learning in Security Sales Value by Country (%), 2023 VS 2030
5.6 Middle East & Africa
5.6.1 Middle East & Africa Deep Learning in Security Sales Value, 2019-2030
5.6.2 Middle East & Africa Deep Learning in Security Sales Value by Country (%), 2023 VS 2030
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Deep Learning in Security Sales Value Growth Trends, 2019 VS 2023 VS 2030
6.2 Key Countries/Regions Deep Learning in Security Sales Value
6.3 United States
6.3.1 United States Deep Learning in Security Sales Value, 2019-2030
6.3.2 United States Deep Learning in Security Sales Value by Type (%), 2023 VS 2030
6.3.3 United States Deep Learning in Security Sales Value by Application, 2023 VS 2030
6.4 Europe
6.4.1 Europe Deep Learning in Security Sales Value, 2019-2030
6.4.2 Europe Deep Learning in Security Sales Value by Type (%), 2023 VS 2030
6.4.3 Europe Deep Learning in Security Sales Value by Application, 2023 VS 2030
6.5 China
6.5.1 China Deep Learning in Security Sales Value, 2019-2030
6.5.2 China Deep Learning in Security Sales Value by Type (%), 2023 VS 2030
6.5.3 China Deep Learning in Security Sales Value by Application, 2023 VS 2030
6.6 Japan
6.6.1 Japan Deep Learning in Security Sales Value, 2019-2030
6.6.2 Japan Deep Learning in Security Sales Value by Type (%), 2023 VS 2030
6.6.3 Japan Deep Learning in Security Sales Value by Application, 2023 VS 2030
6.7 South Korea
6.7.1 South Korea Deep Learning in Security Sales Value, 2019-2030
6.7.2 South Korea Deep Learning in Security Sales Value by Type (%), 2023 VS 2030
6.7.3 South Korea Deep Learning in Security Sales Value by Application, 2023 VS 2030
6.8 Southeast Asia
6.8.1 Southeast Asia Deep Learning in Security Sales Value, 2019-2030
6.8.2 Southeast Asia Deep Learning in Security Sales Value by Type (%), 2023 VS 2030
6.8.3 Southeast Asia Deep Learning in Security Sales Value by Application, 2023 VS 2030
6.9 India
6.9.1 India Deep Learning in Security Sales Value, 2019-2030
6.9.2 India Deep Learning in Security Sales Value by Type (%), 2023 VS 2030
6.9.3 India Deep Learning in Security Sales Value by Application, 2023 VS 2030
7 Company Profiles
7.1 NVIDIA (US)
7.1.1 NVIDIA (US) Profile
7.1.2 NVIDIA (US) Main Business
7.1.3 NVIDIA (US) Deep Learning in Security Products, Services and Solutions
7.1.4 NVIDIA (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.1.5 NVIDIA (US) Recent Developments
7.2 Intel (US)
7.2.1 Intel (US) Profile
7.2.2 Intel (US) Main Business
7.2.3 Intel (US) Deep Learning in Security Products, Services and Solutions
7.2.4 Intel (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.2.5 Intel (US) Recent Developments
7.3 Xilinx (US)
7.3.1 Xilinx (US) Profile
7.3.2 Xilinx (US) Main Business
7.3.3 Xilinx (US) Deep Learning in Security Products, Services and Solutions
7.3.4 Xilinx (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.3.5 Samsung Electronics (South Korea) Recent Developments
7.4 Samsung Electronics (South Korea)
7.4.1 Samsung Electronics (South Korea) Profile
7.4.2 Samsung Electronics (South Korea) Main Business
7.4.3 Samsung Electronics (South Korea) Deep Learning in Security Products, Services and Solutions
7.4.4 Samsung Electronics (South Korea) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.4.5 Samsung Electronics (South Korea) Recent Developments
7.5 Micron Technology (US)
7.5.1 Micron Technology (US) Profile
7.5.2 Micron Technology (US) Main Business
7.5.3 Micron Technology (US) Deep Learning in Security Products, Services and Solutions
7.5.4 Micron Technology (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.5.5 Micron Technology (US) Recent Developments
7.6 Qualcomm (US)
7.6.1 Qualcomm (US) Profile
7.6.2 Qualcomm (US) Main Business
7.6.3 Qualcomm (US) Deep Learning in Security Products, Services and Solutions
7.6.4 Qualcomm (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.6.5 Qualcomm (US) Recent Developments
7.7 IBM (US)
7.7.1 IBM (US) Profile
7.7.2 IBM (US) Main Business
7.7.3 IBM (US) Deep Learning in Security Products, Services and Solutions
7.7.4 IBM (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.7.5 IBM (US) Recent Developments
7.8 Google (US)
7.8.1 Google (US) Profile
7.8.2 Google (US) Main Business
7.8.3 Google (US) Deep Learning in Security Products, Services and Solutions
7.8.4 Google (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.8.5 Google (US) Recent Developments
7.9 Microsoft (US)
7.9.1 Microsoft (US) Profile
7.9.2 Microsoft (US) Main Business
7.9.3 Microsoft (US) Deep Learning in Security Products, Services and Solutions
7.9.4 Microsoft (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.9.5 Microsoft (US) Recent Developments
7.10 AWS (US)
7.10.1 AWS (US) Profile
7.10.2 AWS (US) Main Business
7.10.3 AWS (US) Deep Learning in Security Products, Services and Solutions
7.10.4 AWS (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.10.5 AWS (US) Recent Developments
7.11 Graphcore (UK)
7.11.1 Graphcore (UK) Profile
7.11.2 Graphcore (UK) Main Business
7.11.3 Graphcore (UK) Deep Learning in Security Products, Services and Solutions
7.11.4 Graphcore (UK) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.11.5 Graphcore (UK) Recent Developments
7.12 Mythic (US)
7.12.1 Mythic (US) Profile
7.12.2 Mythic (US) Main Business
7.12.3 Mythic (US) Deep Learning in Security Products, Services and Solutions
7.12.4 Mythic (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.12.5 Mythic (US) Recent Developments
7.13 Adapteva (US)
7.13.1 Adapteva (US) Profile
7.13.2 Adapteva (US) Main Business
7.13.3 Adapteva (US) Deep Learning in Security Products, Services and Solutions
7.13.4 Adapteva (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.13.5 Adapteva (US) Recent Developments
7.14 Koniku (US)
7.14.1 Koniku (US) Profile
7.14.2 Koniku (US) Main Business
7.14.3 Koniku (US) Deep Learning in Security Products, Services and Solutions
7.14.4 Koniku (US) Deep Learning in Security Revenue (US$ Million) & (2019-2024)
7.14.5 Koniku (US) Recent Developments
8 Industry Chain Analysis
8.1 Deep Learning in Security Industrial Chain
8.2 Deep Learning in Security Upstream Analysis
8.2.1 Key Raw Materials
8.2.2 Raw Materials Key Suppliers
8.2.3 Manufacturing Cost Structure
8.3 Midstream Analysis
8.4 Downstream Analysis (Customers Analysis)
8.5 Sales Model and Sales Channels
8.5.1 Deep Learning in Security Sales Model
8.5.2 Sales Channel
8.5.3 Deep Learning in Security Distributors
9 Research Findings and Conclusion
10 Appendix
10.1 Research Methodology
10.1.1 Methodology/Research Approach
10.1.2 Data Source
10.2 Author Details
10.3 Disclaimer

Companies Mentioned: 

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)

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