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2020 Global AI and Machine Learning Operationalization Software Market to Witness a Pronounce Growth by 2025 – News Distinct

Posted on May 15, 2020 by admin

A detailed study on the Global AI and Machine Learning Operationalization Software Market is used for the understanding the strategies, which is used by the manufacturers for increased in changes for the growth of the market in the estimated forecast period. Market research is one of the methods for the determination and estimation of the growth of the Global keyword Market in the estimated forecast period.

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Moreover, sometimes reports are brand specific, depending upon the target audience. They deliver a range of marketing as well as industry research results mainly targeted at the individuals looking forward to invest in the market. The report also covers the detailed analysis of the vendors and the technologies which are being used by the manufacturers for the growth of the market in the estimated forecast period. It also provides detailed analysis of the consumer patterns which are being used and the estimation of the end users in the forecast period for the Global AI and Machine Learning Operationalization Software Market.

Key vendors/manufacturers in the market:

The major players covered in AI & Machine Learning Operationalization Software are:AlgorithmiaDetermined AI5AnalyticsSpellAcusense TechnologiesValohai LtdLogical ClocksDatatron TechnologiesCognitivescaleDreamQuarkParallelMNumericcalIBMWeights & BiasesMLPerfDatabricksImandraPeltarionNeptune LabsIterativeWidgetBrain

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The Global AI and Machine Learning Operationalization Software Market report supplies business outlining, requirements, contact information and product image of important manufacturers of Global AI and Machine Learning Operationalization Software Market. This analysis report similarly reduces the present, past and in future AI and Machine Learning Operationalization Software business strategies, company extent, development, share and estimate analysis having a place with the predicted circumstances. Moreover, the possible results and the exposure to the enhancement of Global AI and Machine Learning Operationalization Software Market widely covered in this report. In conclusion, the AI and Machine Learning Operationalization Software report, demonstrate business enhancement projects, the Global AI and Machine Learning Operationalization Software Market deals network, retailers, consumers, suppliers, research findings, reference section, data sources and moreover.

Global Market By Type:

By Type, AI & Machine Learning Operationalization Software market has been segmented into:Cloud-BasedWeb-Based

Global Market By Application:

By Application, AI & Machine Learning Operationalization Software has been segmented into:Large EnterprisesSMEs

Market research report for every market is based upon several key factors, such as demand and supply of the product, market trends, revenue growth patterns as well as market shares. Report on the Global AI and Machine Learning Operationalization Software Market has been prepared after conducting a comprehensive research through a systematized methodology. These skills are useful for scrutinizing the market on the terms of outlined research guidelines. Mainly, research report covers all the information about the target audience, manufactures, vendors, research papers, products and many more. Moreover, their research papers cover the information and data about an industry in every aspect that consists of information related to the products, services, countries, market size, current trends, business research details and much more. In conclusion, research report gives an overview about all the important information needed to understand about a market. In addition, it is also beneficial and used for the estimation of the several aspects of the market which are likely to have an impact on the growth and the forecast of the market in the estimated forecast period.

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Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

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2020 Global AI and Machine Learning Operationalization Software Market to Witness a Pronounce Growth by 2025 - News Distinct

Machine Learning Market 2020 Business Overview by Manufacturers, Regions, Investment Analysis, Growth Prospects, and Forecast to 2025 – News Distinct

Posted on May 15, 2020 by admin

Machine Learning Market report is to help the user to understand the Coronavirus (COVID19) Impact analysis on market in terms of its Definition, Segmentation, Market Potential, Influential Trends, and the Challenges that the Machine Learning market is facing. The Machine Learning industry profile also contains descriptions of the leading topmost manufactures/players.

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The global Machine Learning Billing Services market is valued at million US$ in 2019 and will reach million US$ by the end of 2026, growing at a CAGR of during 2020-2026. The objectives of this study are to define, segment, and project the size of the Machine Learning Billing Services market based on company, product type, application and key regions.

This report studies the global market size of Machine Learning Billing Services in key regions like North America, Europe, Asia Pacific, Central & South America and Middle East & Africa, focuses on the consumption of Machine Learning Billing Services in these regions

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This research report categorizes the global Machine Learning Billing Services market by players/brands, region, type and application. This report also studies the global market status, competition landscape, market share, growth rate, future trends, market drivers, opportunities and challenges, sales channels, distributors, customers, research findings & conclusion, appendix & data source and Porters Five Forces Analysis.

In addition, this report also contains a price, revenue, market share, and production of the service providers is also mentioned with accurate data. Moreover, the global Machine Learning report majorly focuses on the current developments, new possibilities, advancements, as well as dormant traps. Furthermore, the Machine Learning market report offers a complete analysis of the current situation and the advancement possibilities of the Machine Learning market across the globe. This report analyses substantial key components such as production, capacity, revenue, price, gross margin, sales revenue, sales volume, growth rate, consumption, import, export, technological developments, supply, and future growth strategies.

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Adroit Market Research is an India-based business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a Markets size, key trends, participants and future outlook of an industry. We intend to become our clients knowledge partner and provide them with valuable Market insights to help create opportunities that increase their revenues. We follow a code- Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

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Northern Trust rolls out machine learning tech for FX management solutions – The TRADE News

Posted on May 15, 2020 by admin

Northern Trust has deployed machine learning models within its FX currency management solutions business, designed to enable greater oversight of thoughts of daily data points.

The solution has been developed in partnership with Lumint, an outsourced FX execution services provider, and will help buy-side firms reduce risk throughout the currency management lifecycle.

The technology utilised by the Robotic Oversight System (ROSY) for Northern Trust systematically scans newly arriving, anonymised data to identify anomalies across multi-dimensional data sets. It is also built on machine learning models developed by Lumint using a cloud platform that allows for highly efficient data processing.

In a data-intensive business, ROSY acts like an additional member of the team working around the clock to find and flag anomalies. The use of machine learning to detect data outliers enables us to provide increasingly robust and intuitive solutions to enhance our oversight and risk management, which can be particularly important in volatile markets, said Andy Lemon, head of currency management, Northern Trust.

Northern Trust announced astrategic partnership with Lumint in 2018to deliver currency management services with portfolio, share class and lookthrough hedging solutions alongside transparency and analytics tools.

Northern Trusts deployment of ROSY amplifies the scalability of its already highly automated currency hedging operation; especially for the more sophisticated products such as look-through hedging offered to its global clients, added Alex Dunegan, CEO, Lumint.

The solution is the latest rollout of machine learning technology by Northern Trust, as the bank continues to leverage new technologies across its businesses. In August last year, Northern Trust developed a new pricing engine within its securities lending business by utilising machine learning and advanced statistical technology.

Link:
Northern Trust rolls out machine learning tech for FX management solutions - The TRADE News

Machine Learning in Communication Market Growth Analysis, Share, Demand By Regions, Types And Analysis Of Key Players Research Forecasts To 2025 -…

Posted on May 15, 2020 by admin

Global markets continue to sink as the corona virus spreads, reaching over 200 countries in total by the end of March. Now the outbreak continued to grow, as the number of cases in USA, Italy, Spain, Germany, France all spiked, Europe and USA have now become the epicenter of the outbreak, Cases in China appear have steadied in April, but theres growing concern about the overall impact to the global Machine Learning in Communication market. This study analysis was given on a worldwide scale, for instance, present and traditional Machine Learning in Communication growth analysis, competitive analysis, and also the growth prospects of the central regions. The report gives an exhaustive investigation of Machine Learning in Communication industry at country & regional levels, and provides an analysis of the industry trends in each of the sub-segments, from sales, revenue and consumption. A Machine Learning in Communication quantitative and qualitative analysis of the main players in related regions is introduced, from the perspective of sales, revenue and price.

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According to Orbis Reports, the global Machine Learning in Communication market was valued at USD xxx million in 2019, and it is expected to reach a value of USD xxx million by 2025, at a CAGR of xx% over the forecast period 2021-2025. Correspondingly, the forecast analysis of Machine Learning in Communication industry comprises of Asia, North America, South America, Middle East and Africa, Europe, with the sales and revenue data in each of the sub-segments.

At the upcoming section, Machine Learning in Communication report discusses industrial policy, economic environment, in addition to the fabrication processes and cost structures of the industry. And this report encompasses the fundamental dynamics of the Machine Learning in Communication market which include drivers, opportunities, and challenges faced by the industry. Additionally, this report showed a keen market study of the main consumers, raw material manufacturers and distributors, etc.

In order to stop the spread of the COVID-19 outbreak, countries and world capital have been put under strict lockdown, bringing a total halt to major industrial production chains. It has caused supply chain disruptions for nearly three-quarters of U.S. companies, and in the second quarter, domestically consumption is likely to be hit even harder. The same situation also appeared in Europe, as the epidemic has required large-scale restrictions on the movement of people, investment, consumption and exports will all be strongly impacted by the epidemic, domestic production and consumption will plummet in the first half of 2020. We expected a U-shaped recovery in the second half of the year in USA and Europe market.

China, Japan, South Korea, India, and other Asia countries took the lead in introducing unprecedented measures to contain the virus, the market confidence in Asia-Pacific region is returning, EU and USA have relaxed its fiscal rules with maximum flexibility, this will stimulate the market demand in the second half of 2020.

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Geographically, this report is segmented into several key Regions, with production, consumption, revenue (M USD), market share and growth rate of Machine Learning in Communication in these regions, from 2020 to 2025 (forecast), covering

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)North America (United States, Canada and Mexico)Europe (Germany, France, UK, Russia and Italy)South America (Brazil, Argentina, Columbia)Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Global Machine Learning in Communication market competition by top manufacturers, with production, price, revenue (value) and market share for each manufacturer; the top players including

AmazonIBMMicrosoftGoogleNextivaNexmoTwilioDialpadCiscoRingCentral

On the basis of product, this report displays the production, revenue, price, Machine Learning in Communication market share and growth rate of each type, primarily split into

Cloud-BasedOn-Premise

On the basis on the end users/applications, this report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate of Machine Learning in Communication for each application, including

Network OptimizationPredictive MaintenanceVirtual AssistantsRobotic Process Automation (RPA)

If you have any special requirements, please let us know and we will offer you the report as you want.

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Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: USA: +1 (972)-362-8199 | IND: +91 895 659 5155Email ID: [emailprotected]

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Machine Learning in Communication Market Growth Analysis, Share, Demand By Regions, Types And Analysis Of Key Players Research Forecasts To 2025 -...

Steam now uses machine-learning to tell you what game you should play next – Yahoo News

Posted on May 15, 2020 by admin

Valve is introducing an exciting new Steam Labs experiment to Steam. Experiment 008, officially titled Play Next, is a simple feature that uses machine learning to tell you what game you should play next.

Using machine learning to make informed suggestions, the feature is designed to help users with extensive libraries decide which of their games to play next, stated Valve in its official blog. Users who have unplayed (or very low playtime) games in their library, will now have a Play Next shelf available in the library view.

Though Valve has not told us anything in-depth about the feature, it seems to take information from your recently played games and gives you options based on the similarities in genre and modes. If you usually play FPS games, Steam might recommend different kinds of shooters like battle royales or survival games that are found in your library.

To check out the Play Next feature, make sure you have the latest Steam update installed and the new feature should appear right under your recent games in the Library tab.

READ MORE: This is what Tokido thinks about when he plays Street Fighter

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Steam now uses machine-learning to tell you what game you should play next - Yahoo News

AI/Machine Learning Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 – Cole of Duty

Posted on May 15, 2020 by admin

Amazon

Moreover, the AI/Machine Learning report offers a detailed analysis of the competitive landscape in terms of regions and the major service providers are also highlighted along with attributes of the market overview, business strategies, financials, developments pertaining as well as the product portfolio of the AI/Machine Learning market. Likewise, this report comprises significant data about market segmentation on the basis of type, application, and regional landscape. The AI/Machine Learning market report also provides a brief analysis of the market opportunities and challenges faced by the leading service provides. This report is specially designed to know accurate market insights and market status.

By Regions:

* North America (The US, Canada, and Mexico)

* Europe (Germany, France, the UK, and Rest of the World)

* Asia Pacific (China, Japan, India, and Rest of Asia Pacific)

* Latin America (Brazil and Rest of Latin America.)

* Middle East & Africa (Saudi Arabia, the UAE, , South Africa, and Rest of Middle East & Africa)

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Table of Content

1 Introduction of AI/Machine Learning Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 AI/Machine Learning Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 AI/Machine Learning Market, By Deployment Model

5.1 Overview

6 AI/Machine Learning Market, By Solution

6.1 Overview

7 AI/Machine Learning Market, By Vertical

7.1 Overview

8 AI/Machine Learning Market, By Geography

8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 AI/Machine Learning Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

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AI/Machine Learning Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - Cole of Duty

Shahmir Sanni on Whistleblowing and Corruption Bella Caledonia – bellacaledonia.org.uk

Posted on May 15, 2020 by admin

Previously Chloe Farand maps the nine organisations that Sanni accuses of colluding over a Hard Brexit:

According to Sannis claim, the organisations involved in this right-wing campaign for media coverage include the TaxPayers Alliance, the officer of Peter Whittle, the former deputy leader of UKIP, Civitas, the Adam Smith Institute, Leave Means Leave, the Global Warming Policy Foundation, Brexit Central, the Centre for Policy Studies and the Institute for Economic Affairs.

In the latest Not Another Fake Newscast Sanni talks about the aftermath of his whistleblowing, and being publicly outed by Downing Street. But he says it was not just his homosexuality which was weaponised, his race & religion were too.

All the language that was used around me when I was outed was so racialised, and so inherently rooted in the Islamophobia which has permeated in British society for a long time. People would constantly say he lacks integrity. Sanni compares this to the experience of fellow whistleblowers such as his friend Chris Wylie, Edward Snowden & Chelsea Manning.

Yes there were accusations that they were traitors, but there is this constant coded language that comes with this belief that Pakistanis are fraudulent, that they have money laundering schemes or all live together in a house claiming benefits. These were always tied up with comments about my appearance. Ive never really spoken about this before. Even my outing in itself was racialised. They wouldnt have used that as their big weapon if I wasnt a brown Muslim. It wouldnt be seen as that big a deal, or as a reason to doubt my integrity.

Does Shahmhir think his sexuality was used as a weapon to undermine him within his own community?

Absolutely. Sayeeda Warsi was the only Pakistani politician who reached out to me. There was no Pakistani Labour politicians who reached out to me, no Muslim Labour politicians who reached out to me. And of course this had to do my sexuality. There is homophobia in the Muslim community, in the Pakistani community. There is no denying that.

Shahmirs intervention eventually led to Vote Leave being found guilty of electoral fraud, but the only punishment was a nominal fine. This is what is rotten about the British democratic system. Its essentially whats rotten about this country. No matter what you do to pervert democracy, theres no real ramifications for it. You get a fine and thats it. Theres no further investigation because thats all they can do. Sure you cheated to win the race but you can still keep the medal. Just dont do it again.

This was the biggest breach of campaign finance law in British history. There was a coordinated campaign by the most influential policy groups and research groups who advise the government of the UK on what policies to go for, what decisions to make. Who to give money to and who not to give money to. Which country to bomb and which not to bomb. There was a coordinated effort by these individuals and groups to bury a story, bury information and bury evidence of the single biggest breach of electoral law in British history. I have to remind myself of this sometimes because it seems to have had so little impact. I just have to tell myself that my role is done. I did my part

When it was suggested Shahmir could come up to Scotland and work on an Independence campaign, he had this to say:

Im totally pro-Independence. I am totally pro-Independence. They (the government) hate the Scottish. This is what a lot of people dont understand they genuinely, genuinely do not like Scotland. The whole idea of Scotland they dont like, and this is something I never understood because I grew up in Pakistan. The way that they would talk about the Welsh & the Scottish You really should seek Independence because they hate you. Get out as soon as you can.

Listen to the full interview here

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Shahmir Sanni on Whistleblowing and Corruption Bella Caledonia - bellacaledonia.org.uk

What is Cryptography? | Cryptographic Algorithms | Types …

Posted on May 15, 2020 by admin

Encryption is essentially important because it secures data and information from unauthorized access and thus maintains the confidentiality. Heres a blog post to help you understand what is cryptography and how can it be used to protectcorporate secrets, secure classified information, and personal information to guard against things like identity theft.

Heres what I have covered in this blog:

You can go through this What is Cryptography video where our Cybersecurity Trainingexpert is discussing each & every nitty-gritty of the technology.

Now, Im going to take help of an example or a scenario to explain what is cryptography?

Lets say theres a person namedAndy.Now suppose Andy sends a message to his friend Sam who is on the other side of the world. Now obviously he wants this message to be private and nobody else should have access to the message. He uses a public forum, for example, WhatsApp for sending this message. The main goal is to secure this communication.

Lets say there is a smart guy called Eaves who secretly got access to your communication channel. Since this guy has access to your communication, he can do much more than just eavesdropping, for example, he can try to change the message. Now, this is just a small example. What if Eave gets access to your private information? The result could be catastrophic.

So how can Andy be sure that nobody in the middle could access the message sent to Sam? Thats where Encryption orCryptographycomes in. Let me tell you What is Cryptography .

Cryptography is the practice and study of techniques for securing communication and data in the presence of adversaries.

Alright, now that you know what is cryptography lets see how cryptography can help secure the connection between Andy and Sam.

So, to protect his message, Andy first convert his readable message to unreadable form. Here, he converts the message to some random numbers. After that, he uses a key to encrypt his message, in Cryptography, we call this ciphertext.

Andy sends this ciphertext or encrypted message over the communication channel, he wont have to worry about somebody in the middle of discovering his private messages. Suppose, Eaves here discover the message and he somehow manages to alter it before it reaches Sam.

Now, Sam would need a key to decrypt the message to recover the original plaintext. In order to convert the ciphertext into plain text, Sam would need to use the decryption key. Using the key he would convert the ciphertext or the numerical value to the corresponding plain text.

After using the key for decryption what will come out is the original plaintext message, is an error. Now, this error is very important. It is the way Sam knows that message sent by Andy is not the same as the message that he received. Thus, we can say that encryption is important to communicate or share information over the network.

Now, based on the type of keys and encryption algorithms, cryptography is classified under the following categories:

Cryptography is broadly classified into two categories: Symmetric key Cryptography and Asymmetric key Cryptography (popularly known as public key cryptography).

Now Symmetric key Cryptography is further categorized as Classical Cryptography and Modern Cryptography.

Further drilling down, Classical Cryptography is divided into Transposition Cipher and Substitution Cipher. On the other hand, Modern Cryptography is divided into Stream Cipher and Block Cipher.

So, lets understand these algorithms with examples.

Lets start with the Symmetric key encryption

Anencryptionsystem in which the sender and receiver of a message share a single, commonkeythat is used to encrypt and decrypt the message. The most popularsymmetrickeysystem is the DataEncryptionStandard (DES)

In Cryptography, a transposition cipher is a method of encryption by which the positions held by units of plaintext (which are commonly characters or groups of characters) are shifted according to a regular system, so that the ciphertext constitutes a permutation of the plaintext.

That is, the order of the units is changed (the plaintext is reordered). Mathematically, a bijective function is used on the characters positions to encrypt and an inverse function to decrypt.

Example:

Method of encryption by which units of plaintext are replaced with ciphertext, according to a fixed system; the units may be single letters (the most common), pairs of letters, triplets of letters, mixtures of the above, and so forth.

Consider this example shown on the slide: Using the system just discussed, the keyword zebras gives us the following alphabets:

Symmetric or secret-key encryption algorithm that encrypts a single bit at a time. With a Stream Cipher, the same plaintext bit or byte will encrypt to a different bit or byte every time it is encrypted.

An encryption method that applies a deterministic algorithm alongwith a symmetric key to encrypt a block of text, rather than encrypting one bit at a time as in stream ciphers

Example: Acommon block cipher, AES, encrypts 128-bit blocks with a key of predetermined length: 128, 192, or 256 bits. Block ciphers are pseudorandom permutation (PRP) families that operate on the fixed size block of bits. PRPs are functions that cannot be differentiated from completely random permutations and thus, are considered reliable until proven unreliable.

The encryption process where different keys are used for encrypting and decrypting the information. Keys are different but are mathematically related, such that retrieving the plain text by decrypting ciphertext is feasible.

RSA is the most widely used form of public key encryption,

Heres how keys are generated in RSA algorithm

Alright, this was it for What is Cryptography blog. To safeguard your information and data shared over the internet it is important to use strong encryption algorithms, to avoid any catastrophic situations.

If you wish to learn Cybersecurity and build a colorful career in cybersecurity, then check out ourCybersecurity Certification Trainingwhichcomes with instructor-led live training and real-life case studies experience.This training will help you in becoming a Cybersecurity expert.

Also, learn Cybersecurity the right way with Edurekas POST GRADUATE PROGRAMwithNIT Rourkela and defend the worlds biggest companies from phishers, hackers and cyber attacks.

Got a question for us? Please mention it in the comments section and we will get back to you.

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What is Cryptography? | Cryptographic Algorithms | Types ...

Cryptography | An Open Access Journal from MDPI

Posted on May 15, 2020 by admin

Large-scale quantum computing poses a major threat to classical public-key cryptography. Recently, strong quantum access security models have shown that numerous symmetric-key cryptosystems are also vulnerable. In this paper, we consider classical encryption in a model that grants the adversary quantum oracle access [...] Read more.Large-scale quantum computing poses a major threat to classical public-key cryptography. Recently, strong quantum access security models have shown that numerous symmetric-key cryptosystems are also vulnerable. In this paper, we consider classical encryption in a model that grants the adversary quantum oracle access to encryption and decryption, but where we restrict the latter to non-adaptive (i.e., pre-challenge) queries only. We formalize this model using appropriate notions of ciphertext indistinguishability and semantic security (which are equivalent by standard arguments) and call it QCCA 1 in analogy to the classical CCA 1 security model. We show that the standard pseudorandom function ( PRF )-based encryption schemes are QCCA 1 -secure when instantiated with quantum-secure primitives. Our security proofs use a strong bound on quantum random-access codes with shared randomness. Revisiting plain IND CPA -secure Learning with Errors ( LWE ) encryption, we show that leaking only a single quantum decryption query (and no other leakage or queries of any kind) allows the adversary to recover the full secret key with constant success probability. Information-theoretically, full recovery of the key in the classical setting requires at least a linear number of decryption queries. Our results thus challenge the notion that LWE is unconditionally just as secure quantumly as it is classically. The algorithm at the core of our attack is a new variant of the well-known BernsteinVazirani algorithm. Finally, we emphasize that our results should not be interpreted as a weakness of these cryptosystems in their stated security setting (i.e., post-quantum chosen-plaintext secrecy). Rather, our results mean that, if these cryptosystems are exposed to chosen-ciphertext attacks (e.g., as a result of deployment in an inappropriate real-world setting) then quantum attacks are even more devastating than classical ones.Full article

Excerpt from:
Cryptography | An Open Access Journal from MDPI

I, CyBOK – Introduction to the Cyber Security Body of Knowledge Project – tripwire.com

Posted on May 15, 2020 by admin

The Cyber Security Body of Knowledge project or CyBOK is a collaborative initiative mobilised in 2017 with an aspiration to codify the foundational and generally recognized knowledge on Cyber Security. Version 1.0 of the published output of this consultative exercise was quietly released last year and then more publicly launched in January 2020.

Yet, this free and information-packed publication does not appear to have captured the attention it perhaps deserves across the wider industry. Hence the reason for blogging and discussing a very quick overview of it here on State of Security. So, what does it look like?

Across its 800+ pages, the CyBOK is effectively organized into nineteen top-level Knowledge Areas (KAs) and then grouped into five overarching categories, as shown in this diagram.

Much of this will be familiar territory for many security professionals, some of whom have actually questioned if it is not simply reinventing the wheel? (ISC) has after all, already established a widely recognized Common Body of Knowledge or CBK for its Certified Information Systems Security Professional (CISSP) accreditation. For those unfamiliar, the overarching CISSP CBK domain categories, are:

Originating in the early 1990s before the term Cyber was common parlance for IT related security matters, the (ISC) CBK has more traditionally been known by many as a Common Body of Knowledge for Information Security of course.

Whereas the CyBOK begins by offering distinct definitions for both Information Security and Cyber Security, presenting the former as a contributor to the latter. Yet, there is an inevitable overlap of knowledge and topics across both taxonomies, just as there is within their actual practices in the real world of course.

Given also, that this is the definition which the CyBOK uses in its introduction:

Cyber security refers to the protection of information systems (hardware,software and associated infrastructure), the data on them, and the services they provide,from unauthorised access, harm or misuse. This includes harm caused intentionallyby the operator of the system, or accidentally, as a result of failing to follow securityprocedures.

Such a definition could apply just as relevantly to much of the CISSP CBK, however. Blurring these arguably subjective lines further, (ISC) have more recently taken to promoting CISSP as being the worlds premier cybersecurity certification.

A less semantic and perhaps more useful differentiator to consider instead, is that the CISSP CBK is also a curriculum for the certification itself. Although sometimes disingenuously described as being an inch deep and a mile wide, it is, in reality, more a mile wide and a foot, or even yard deep in certain places.

The CyBOK instead seeks to map to established knowledge sets via a structured framework. This mapping may then be used to inform and underpin education and professional training for the cyber security sector.

The opening narrative of the Law & Regulation category someway acknowledges this by disclaiming itself to be a mere starting rather than ending point and the same could be said to apply throughout the CyBOK.

But that is not to say it is just some dry reference manual of other works. The clear expository narratives which accompany each of the knowledge areas are all original, insightful and very readable. Likewise, the quality of expertise drawn upon to create the diverse Knowledge Areas in their own right and then collate all of this into one cohesive publication should not be underestimated.

Moreover, it positions itself as vendor agnostic, academically independent and, whilst sponsored by the UKs National Cyber Security Programme, a cross-border effort of trans-global rather than marginalized national focus.

The CyBOK also seeks to gather a balance of input from both academia and industry. With its prolific use of functional equations and theoretical models throughout the text, it does come across as being more at home within the classroom or laboratory environment than the operational, business driven frontline at times.

But as with the CISSP CBK once again, such an approach is for some areas both appropriate and somewhat inevitable. Cryptography for example, is an essentially mathematically rooted subject area. The KA for cryptography therefore warrants a suitably scholarly approach to both its curation and to the prefatory descriptions of some of the core concepts as they relate to cyber.

Likewise, the Malware KA with its lab eye view of its subject matter, descriptively dissects characteristics and tactics of different malware families whilst discussing some of the analysis techniques used to understand them. It goes on to include clear, succinct explanations of some common anti-analysis and detection evasion techniques such as packing (compressing or encrypting part of the code.). These are base concepts for sure, but they are often glossed over in more overtly sales focused, industry publications on malware.

Such key technical considerations are then complemented and contextually framed by a brief introduction of the Underground Eco-System driving the ever-evolving malware lifecycle itself. Underground economics, monetization and black-market operating models all being cross-cutting themes discussed elsewhere, such as in the subsequent KA for Adversarial Behaviours.

The Forensics KA similarly offers a high-quality potted summary of key concepts, tools and methods as they are used to establish evidence in legal proceedings. It introduces some relevant cognitive, conceptual models such as the sense-making and foraging loops and then moves into describing specific analytical techniques and methods. Bringing its subject matter firmly up to date, it concludes by acknowledging the transition and challenges that cloud computing & IoT brings to the science of digital forensics.

The Security Operations & Incident Management (SOIM) KA provides a solid representation of many of the key principles and components one would expect to be included for SOC type considerations. From base architectural principles to logs, network flows, anomaly detection, IDS/IPS, SIEM, SOAR.

Leading into an overview of Incident Management planning and process groundwork. In places some of it is very well-trodden ground which could probably benefit from wider and more diverse contemporary industry input. Accepting of course that only so much consultation is feasible and affordable for a single project and such an undertaking is easier said than done.

However, what is covered here is covered very well. Its precise and authoritative narrative describing what good practice can look like whilst acknowledging the inherent fallibility of many tools, techniques and processes in detecting and stopping all threats or achieving the nirvana of total security. A state that is of course impossible, as it acknowledges from the outset in the referencing of a report from 1981 by James Anderson.

All in all, the sheer breadth of information condensed into this one collective work is as impressive as it is vast. Whilst Ive cherry picked just a few of the KAs to highlight here, it would be futile to try and discuss every single one in a short blog such as this, let alone do any reasonable justice to any of them. But dont just take my word or views about it, take a look for yourself. The CyBOK is freely available and accessible under the open government license, so theres really no excuse not to.

Admittedly, for many people its probably not a cover to cover read (unless you are perhaps landed with a lot of time on your hands as a result of the pandemic lockdowns.) For professionals or anyone curious to understand more about the diverse range of knowledge areas which collectively define and support what we have come to call Cyber Security. it is at the very least a useful reference to dip into as necessary.

Given the comprehensive mapping it also gives you to a wealth of established knowledge sets, papers and other references (all helpfully linked to directly in its bibliography) who knows where it may lead you next?

About the Author:Angus Macraeis a Certified Information Systems Security Professional (CISSP) in good standing. He has more recently been awarded the CESG Certified Professional IT Security Officer (ITSO ) role at Senior Practitioner level. He is currently lucky enough to live in and publicly serve the beautiful county of Cornwall in the UK.

Editors Note:The opinions expressed in this guest author article are solely those of the contributor, and do not necessarily reflect those of Tripwire, Inc.

Read the original here:
I, CyBOK - Introduction to the Cyber Security Body of Knowledge Project - tripwire.com

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