Big Data, Data Science, Machine Intelligence and Learning: Demystification, Challenges, Opportunities and Principles for Success in the Context of (Data-Driven) Cybersecurity

Big Data, Data Science, Machine
Intelligence and Learning
Demystiﬁcation, Challenges, Opportunities and
Principles for Success in the Context of
(Data-Driven) Cybersecurity
Prof. Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting, Berne & GSEM, University of Geneva, Switzerland
@DiegoKuonen + kuonen@statoo.com + www.statoo.info
‘Keynote @ Swiss Cyber Security Days 2019’, Fribourg, Switzerland — February 28, 2019

About myself (about.me/DiegoKuonen)
PhD in Statistics, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
MSc in Mathematics, EPFL, Lausanne, Switzerland.
• CStat (‘Chartered Statistician’), Royal Statistical Society, UK.
• PStat (‘Accredited Professional Statistician’), American Statistical Association, USA.
• CSci (‘Chartered Scientist’), Science Council, UK.
• Elected Member, International Statistical Institute, NL.
• Senior Member, American Society for Quality, USA.
• President of the Swiss Statistical Society (2009-2015).
Founder, CEO & CAO, Statoo Consulting, Switzerland (since 2001).
Professor of Data Science, Research Center for Statistics (RCS), Geneva School of Economics
and Management (GSEM), University of Geneva, Switzerland (since 2016).
Founding Director of GSEM’s new MSc in Business Analytics program (started fall 2017).
Principal Scientific and Strategic Big Data Analytics Advisor for the Directorate and Board of
Management, Swiss Federal Statistical Office (FSO), Neuchâtel, Switzerland (since 2016).
Copyright c 2001–2019, Statoo Consulting, Switzerland. All rights reserved.
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About Statoo Consulting (www.statoo.info)
• Founded Statoo Consulting in 2001.
2019 − 2001 = 18 + .
• Statoo Consulting is a software-vendor independent Swiss consulting ﬁrm
specialised in statistical consulting and training, data analysis, data mining
(data science) and big data analytics services.
• Statoo Consulting oﬀers consulting and training in statistical thinking, statistics,
data mining and big data analytics in English, French and German.
Are you drowning in uncertainty and starving for knowledge?
Have you ever been Statooed?

‘Data is arguably the most important natural
resource of this century. ... Big data is big news just
about everywhere you go these days. Here in Texas,
everything is big, so we just call it data.’
Michael Dell, 2014
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1. Demystifying the ‘big data’ hype
• The term ‘big data’ — coined in 1997 by two researchers at the NASA — has
acquired the trappings of a ‘religion’.
• But, what exactly are ‘big data’?
The term ‘big data’ applies to an accumulation of data that can not be
processed or handled using traditional data management processes or tools.
Big data are a data management IT infrastructure which should ensure that the
underlying hardware, software and architecture have the ability to enable ‘learning
from data’ or ‘making sense out of data’, i.e. ‘analytics’ ( ‘data-driven decision
making’ and ‘data-driven cybersecurity’).
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Data-driven cybersecurity starts with data management!
Examples of cybersecurity data sources:
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Examples of cybersecurity data access and usage:
Source: Christopher Smith & Evan Levy, ‘Stronger Cybersecurity Starts with Data Management’,
International Institute for Analytics, 2016 (goo.gl/3oogK8).
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‘Veracity’ (i.e. ‘trust in data’), including the reliability (‘quality over time’),
capability, validity and security of the data, and related quality of the data are key!
Existing ‘small’ data quality frameworks need to be extended, i.e. augmented!
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‘Data is part of Switzerland’s infrastructure, such as
road, railways and power networks, and is of great
value. The government and the economy are obliged
to generate added value from these data.’
digitalswitzerland, November 22, 2016
Source: digitalswitzerland’s ‘Digital Manifesto for Switzerland’ (digitalswitzerland.com).
The 5th V of big data: ‘Value’ , i.e. the ‘usefulness of data’.
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Intermediate summary: the ‘ﬁve Vs’ of (big) data
‘Volume’, ‘Variety’ and ‘Velocity’ are the ‘essential’ characteristics of (big) data;
‘Veracity’ and ‘Value’ are the ‘qualiﬁcation for use’ characteristics of (big) data.
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2. Demystifying the ‘Internet of things’ hype
• The term ‘Internet of Things’ (IoT) — coined in 1999 by the technologist Kevin
Ashton — starts acquiring the trappings of a ‘new religion’!
Source: Christer Bodell, ‘SAS Institute and IoT’, May 30, 2017 (goo.gl/cVYCKJ).
However, IoT is about data, not things!
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The ‘ﬁve Vs’ of IoT (data)
‘Volume’, ‘Variety’ and ‘Velocity’ are the ‘essential’ characteristics of IoT (data);
‘Veracity’ and ‘Value’ are the ‘qualiﬁcation for use’ characteristics of IoT (data).
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‘Data are not taken for museum purposes; they are
taken as a basis for doing something. If nothing is to
be done with the data, then there is no use in
collecting any. The ultimate purpose of taking data
is to provide a basis for action or a recommendation
for action.’
W. Edwards Deming, 1942
Data are the fuel and analytics, i.e. ‘learning from data’ or ‘making sense out
of data’, is the engine of the digital transformation and data-driven cybersecurity!
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3. Demystifying the two approaches of analytics
Statistics, data science and their connection
Statistics traditionally is concerned with analysing primary (e.g. experimental or
‘made’ or ‘designed’) data that have been collected (and designed) for statistical
purposes to explain and check the validity of specific existing ‘ideas’ (‘hypotheses’),
i.e. through the operationalisation of theoretical concepts.
Primary analytics or top-down (i.e. explanatory and confirmatory) analytics.
‘Idea (hypothesis) evaluation or testing’ .
Analytics’ paradigm: ‘deductive reasoning’ as ‘idea (theory) ﬁrst’.
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Data science — a rebranding of ‘data mining’ and as a term coined in 1997 by a
statistician — on the other hand, typically is concerned with analysing secondary
(e.g. observational or ‘found’ or ‘organic’ or ‘convenience’) data that have been
collected (and designed) for other reasons (and often not ‘under control’ or
without supervision of the investigator) to create new ideas (hypotheses or
theories).
Secondary analytics or bottom-up (i.e. exploratory and predictive) analytics.
‘Idea (hypothesis) generation’ .
Analytics’ paradigm: ‘inductive reasoning’ as ‘data ﬁrst’.
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‘Spurious correlation is not causation!’
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‘Any claim coming from an observational study is
most likely to be wrong.’
S. Stanley Young and Alan Karr, 2011
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• The two approaches of analytics, i.e. inductive and deductive reasoning, are
complementary and should proceed iteratively and side by side in order to enable
data-driven decision making, data-driven cybersecurity and proper
continuous improvement.
The inductive–deductive reasoning cycle:
Source: Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799.
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‘Neither exploratory nor confirmatory is sufficient
alone. To try to replace either by the other is
madness. We need them both.’
John W. Tukey, 1980
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‘Welcome to the Machine’ (Pink Floyd, 1975)
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4. Demystifying the ‘machine intelligence and learning’ hype
John McCarthy, one of the founders of ‘Artificial Intelligence’ (AI) (now
sometimes referred to as ‘Machine Intelligence’, MI) research, defined in 1956 the
field of AI as ‘getting a computer to do things which, when done by people, are said
to involve intelligence’, e.g. visual perception, speech recognition, language
translation, visual translation and playing games (with concrete rules).
AI is about (smart) machines capable of performing tasks normally performed by
humans ( ‘learning machines’), i.e. ‘making machines smart’.
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In 1959, Arthur Samuel deﬁned ‘Machine Learning’ (ML) as one part of a larger
AI framework ‘that gives computers the ability to learn’.
ML explores the study and construction of algorithms that can learn from and
make predictions on (yet-to-be-seen) data, i.e. ‘prediction making’ through the use
of computers, and help make decisions.
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Source: ‘Historical cost of computer memory and storage’ (hblok.net/blog/storage/).
27

Some examples of common (data-driven) cybersecurity use cases of such
‘(machine) learning systems’:
28

Source: Guerra, P. & Tamburello. P. (2018). Modernizing Cybersecurity Operations
with Machine Intelligence. O’Reilly Media, Sebastopol, CA.
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An example of a machine learning workﬂow
Monitoring and using the resulting feedback are at the core of machine learning.
Implementation requires the automation of the monitoring step and the feedback
ingestion step. Assuming this is done, we have a ‘learning machine’ .
Source: Jean-Francois Puget, Chief Architect, IBM Analytics Solutions, ‘Machine learning
algorithm = learning machine’, April 27, 2016 (goo.gl/7nK4pR).
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• However, without humans as a guide, current AI is no more capable than a computer
without software!
• AI without trustworthy data is like a swimming pool without trustworthy water!
• There is nothing artificial about AI: it is inspired by humans, it is created by humans
and impacts humans!
• Data-driven cybersecurity starts with trust, simple because data collected for
analytics must be trusted!
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‘Business is not chess; smart machines alone can not
win the game for you. The best that they can do for
you is to augment the strengths of your people.’
Thomas H. Davenport, August 12, 2015
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‘AI algorithms are not natively ‘intelligent’. They
learn inductively by analyzing data. ... Sophisticated
algorithms can sometimes overcome limited data if
its quality is high, but bad data is simply paralyzing.’
Sam Ransbotham, David Kiron, Philipp Gerbert and Martin Reeves, 2017
Source: Ransbotham, S., Kiron, D., Gerbert, P. & Reeves M. (2017). Reshaping Business With Artiﬁcial
Intelligence. MIT Sloan Management Review & The Boston Consulting Group (goo.gl/wnGqr3).
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• The largest and most basic ‘need’ in the analytics hierarchy is the need for a
‘strong’ data collection (Monica Rogati, 2017; goo.gl/F7hKH7):
Data should be treated as a key strategic asset, so ensuring their veracity
and the related data quality become imperative!
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‘Data themselves are a central raw material of the
knowledge society. However, this means that the data
must be of high quality, accessible and trustworthy.’
Swiss Federal Council, September 5, 2018
Source: ‘Digitale Schweiz’ strategy, adopted by the Federal Council on September 5, 2018 (goo.gl/b7K8aE).
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Challenges, opportunities and principles for success
• In a world of (big) data and IoT (data), the veracity of data, i.e. the trustworthiness
of data (including the related data quality), is more important than ever!
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• Analytics is an aid to thinking and not a replacement for it!
• Data and analytics should be envisaged to complement and augment humans to
strengthen existing cybersecurity setups, e.g. by extending existing security rules, and
not replacements for them!
Humans (and cybersecurity teams as well!) need to augment their strengths
to become more ‘powerful’: by automating any routinisable work and by focusing
on their core competences.
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My key principles for data-driven cybersecurity’s success
• Do not neglect the following four principles that ensure successful outcomes:
– use of sequential approaches to problem solving and improvement, as studies
are rarely completed with a single data set but typically require the sequential
analysis of several data sets over time ( ‘continuous improvement’);
– having a strategy for the project and for the conduct of the data analysis;
including thought about the ‘business’ objectives ( ‘strategic thinking’ );
– carefully considering data quality and assessing the ‘data pedigree’ before,
during and after the data analysis; and
– applying sound subject matter knowledge (‘domain knowledge’ or ‘business
knowledge’, i.e. knowing the ‘business’ context, process and problem to which
analytics will be applied), which should be used to help deﬁne the problem, to
assess the data pedigree, to guide data analysis and to interpret the results.
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‘All improvement takes place project by project and
in no other way.’
Joseph M. Juran, 1989
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‘If you can not describe what you are doing as a
process, you do not know what you are doing.’
W. Edwards Deming
Analytics is is not only modelling and prediction, nor a product that can be
bought, but a whole iterative problem solving and improvement cycle/process that
must be mastered through interdisciplinary and transdisciplinary team effort.
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The complementary cycles of developing & deploying ‘analytical assets’
Source: Erick Brethenoux, Director, IBM Analytics Strategy & Initiatives, August 18, 2016 (goo.gl/AhsG1n).
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‘It is getting better. . . A little better all the time.’
The Beatles, 1967
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‘You do not need a digital strategy. You need a
better (‘business’) strategy, enabled by digital. ’
George Westerman, 2018
Source: Westerman, G. (2018). Your company doesn’t need a digital strategy.
MIT Sloan Management Review, 59(3), 14–15 (goo.gl/mSb5yd).
Cybersecurity is not about the technologies (which change too quickly)!
Focus on transformation instead of technology!
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Source: Boehm, T., Merrath, P., Poppensieker, T., Riemenschnitter, R. & St¨ahle, T. (2018). Cyber Risk
Measurement and the Holistic Cybersecurity Approach. McKinsey & Company (goo.gl/2Dj2M7).
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‘We can not solve problems by using the same kind
of thinking we used when we created them.’
Albert Einstein
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‘The transformation can only be accomplished by
man, not by hardware (computers, gadgets,
automation, new machinery). A company can not
buy its way into quality.’
W. Edwards Deming, 1982
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‘The only person who likes change is a wet baby.’
Mark Twain
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Have you been Statooed & GSEMed?
Prof. Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting GSEM, University of Geneva
Morgenstrasse 129 Bd du Pont-d’Arve 40
3018 Berne 1211 Geneva 4
Switzerland
email kuonen@statoo.com Diego.Kuonen@unige.ch
web www.statoo.info gsem.unige.ch/rcs/kuonen
@DiegoKuonen
Presentation code: ‘SCSD.Feb.2019’. Typesetting: LATEX, version 2 . PDF producer: pdfTEX, version 3.141592-1.40.3-2.2. Compilation date: 27.02.2019.

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