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Who can protect us? | Enterprise adoption and cybersecurity issues in Internet of Things (IoT) connectivity

Publication | Update:

Feb 2020

Cisco is estimating that the "Internet of Everything cisco article" could have has many as 50 billion connected devices by 2020.

According to F-Secure, a cybersecurity company citing research from Gartner, over the next two years, the number of IoT devices entering households will climb steeply from under 15 devices per household currently to 500 by 2022, with IoT connectivity being bundled into products.

According to Mikko Hypponen, chief research officer for F-Secure in research on the IoT, in the future, devices without IoT capabilities may be more expensive because they'll lack data that can be harvested by manufacturers.

According to David Roe of CMS Wire while security is one of the major issues impacting the development, there are a number of other issues to consider.

Major issues for enterprises connecting to the IoT.

According to the World Economic Forum, governments consider breaking up the internet in national territories. There are other pressures too that will push them to do this, including economic protectionism, regulatory divergence and the loss of government power relative to global online companies. This will create barriers to the flow of content and transactions. “Some might welcome a move towards a less hyper-globalized online world, but many would not, resistance would be likely, as would the rapid growth of illegal workarounds. The pace of technological development would slow and its trajectory would change,” says the report.

While there is growing awareness of the Cloud Attacks problem, cybersecurity is still under-resourced in comparison to the potential scale of the threat. World Economic Forum report cites analysis that suggests that the takedown of a single cloud provider could cause $ 50 billion to $ 120 billion of economic damage.

Although the threat magnitude of ransomware has already grown 35 times over the last year with ransomworms and other types of attacks, there is more to come. Derek Manky, global security strategist at Sunnyvale, Calif.-based Fortinet agrees that the problems for cloud vendors are only emerging, and said that the next big target for ransomware is likely to be cloud service providers and other commercial services with a goal of creating revenue streams. The complex, hyperconnected networks cloud providers have developed can produce a single point of failure for hundreds of businesses, government entities, critical infrastructures, and healthcare organizations.

Millions of new connected consumer devices make a wide attack surface for hackers, who will continue to probe the connections between low-power, somewhat dumb devices and critical infrastructure, Shaun Cooley, VP and CTO of Cisco  said. The biggest security challenge he sees is the creation of Distributed Destruction of Service (DDoS) attacks that employ swarms of poorly-protected consumer devices to attack public infrastructure through massively coordinated misuse of communication channels. 

IoT botnets care capable of causing damaging and unpredictable spikes in infrastructure use, leading to things like power surges, destructive water attacks, or reduced availability of critical infrastructure on a city level. Solutions for these attacks do exist, from smarter control software that can tell the difference between emergency and erroneous sensor data, and standards that put bounds on what data devices are allowed to send, or how often they're allowed to send it. But the challenge of securing consumer-grade sensors and devices remains, especially as they increasingly connect, to our shared infrastructure. 

AJ Abdallat is CEO of Beyond Limits, points out that most of the current AI offerings on the market have substantial limits.  As big data and machine learning powered AI’s gains processing power, they can incorporate into their algorithms more and more information, more and more variables that may affect data associations. But with little human intervention, inevitably some variables may display strong correlation by pure chance, with little actual predictive effect. 

The practical applications of AI to the IoT include, Smart IoT that connects and optimizing devices, data and the IoT; AI-Enabled Cybersecurity that offers data security encryption and enhanced situational awareness to provide document, data, and network locking using smart distributed data secured by an AI key.

Gemalto a cybersecurity firm that has researched the impact of security on the development of the IoT, found that that 90 percent of consumers lack confidence in the security of Internet of Things devices. This comes as more than two-thirds of consumers and almost 80% of organizations support governments getting involved in setting IoT security. Its State of IoT Security research report, showed the following data.

  • 96 percent of businesses and 90 percent of consumers believe there should be IoT security regulations
  • 54 percent of consumers own an average of four IoT devices, but only 14 percent believe that they are knowledgeable on IoT device security
  • 65 percent of consumers are concerned about a hacker controlling their IoT device, while 60 percent are concerned about data being leaked

"It's clear that both consumers and businesses have serious concerns around IoT security and little confidence that IoT service providers and device manufacturers will be able to protect IoT devices and more importantly the integrity of the data created, stored and transmitted by these devices," said Jason Hart, CTO of Data Protection at Gemalto said in a statement about the report. "Until there is confidence in IoT amongst businesses and consumers, it won't see mainstream adoption.”

According to David Roe of CMS Wire the real issue is how to increase the ability for people to understand the changes and their implications more clearly, and to take concrete actions to take advantage of the potential upside. Internet of Things is moving into its adolescence as connected devices become smarter and more immersive, and expectations to convert IoT data to insights and financial value increase. Also, algorithms and data visualization templates have evolved so that new use cases can take advantage of earlier ones.

"The pace of change has exceeded the rate of human capability to absorb — the cup is already full," said Jeff Kavanaugh, VP and Senior Partner in High Tech & Manufacturing for Infosys.

And no doubt, the exponential adoption of IoT will drive down sensor and acquisition costs, enabling more and more viable business cases that have previously been too expensive. 

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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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