[Syngress advanced topics in information security] Layton, Robert_ Watters, Paul A - Automating open source intelligence _ algorithms for OSINT (2016, Elsevier, Syngress).pdf

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Automating Open
Source Intelligence
Algorithms for OSINT
Edited By
Robert Layton
Paul A. Watters
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List of Contributors
Brenda Chawner
School of Information Management, Victoria Business
School, Victoria University of Wellington, New Zealand
Shadi Esnaashari
School of Engineering and Advanced Technology, Massey
University, Auckland, New Zealand
Ernest Foo
School of Electrical Engineering and Computer Science – Science
and Engineering Faculty, Queensland University of Technology, Queensland,
Australia
Rony Germon
PSB Paris School of Business, Chair Digital Data Design
Iqbal Gondal
Internet Commerce Security Laboratory, Federation University,
Australia
Hans Guesgen
School of Engineering and Advanced Technology, Massey
University, New Zealand (Palmerston North campus)
Christian Kopp
Internet Commerce Security Laboratory, Federation Univer-
sity, Australia
Robert Layton
Internet Commerce Security Laboratory, Federation University,
Australia
Seung Jun Lee
School of Engineering & Advanced Technology, Massey
University, New Zealand
Charles Perez
PSB Paris School of Business, Chair Digital Data Design
Agate M. Ponder-Sutton
Information Technology & Centre for Information
Technology, School of Engineering and Advanced Technology, Massey Univer-
sity, New Zealand
Jim Sillitoe
Internet Commerce Security Laboratory, Federation University,
Australia
Jason Smith
School of Electrical Engineering and Computer Science – Science
and Engineering Faculty, Queensland University of Technology, Queensland,
Australia
Kristin Stock
School of Engineering and Advanced Technology, Massey Uni-
versity, New Zealand (Albany, Auckland campus)
ix
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List of Contributors
Suriadi Suriadi
School of Engineering and Advanced Technology, College of
Sciences, Massey University, New Zealand
Paul A. Watters
School of Engineering & Advanced Technology, Massey
University, New Zealand
George R.S. Weir
Department of Computer and Information Sciences,
University of Strathclyde, Glasgow, UK
Ian Welch
School of Engineering and Computer Science, Victoria University
of Wellington, New Zealand
C H A P T ER 1
The Automating of Open Source
Intelligence
Agate M. Ponder-Sutton
Information Technology & Centre for Information Technology, School of Engineering
and Advanced Technology, Massey University, New Zealand
Open source intelligence (OSINT) is intelligence that is synthesized using pub-
licly available data (Hobbs,
Moran, & Salisbury, 2014).
It differs significantly
from the open source software movement. This kind of surveillance started
with the newspaper clipping of the first and second world wars. Now it is ubiq-
uitous within large business and governments and has dedicated study. There
have been impassioned, but simplified, arguments for and against the current
levels of open source intelligence gathering. In the post-Snowden leaks world
one of the questions is how to walk the line between personal privacy and
nation state safety. What are the advances? How do we keep up, keep relevant,
and keep it fair or at least ethical? Most importantly, how do we continue to
“make sense or add value” as Robert David Steele would say, (http://tinyurl.
com/EIN-UN-SDG).
I will discuss the current state of OSINT and data science.
The changes in the analysts and users will be explored. I will cover data anal-
ysis, automated data gathering, APIs, and tools; algorithms including super-
vised and unsupervised learning, geo-locational methods, de-anonymization.
How do these interactions take place within OSINT when including ethics and
context? How does OSINT answer the challenge laid down by Schneier in his
recent article elaborating all the ways in which big data have eaten away at the
privacy and stability of private life, “Your cell phone provider tracks your loca-
tion and knows who is with you. Your online and in-store purchasing patterns
are recorded, and reveal if you are unemployed, sick, or pregnant. Your emails
and texts expose your intimate and casual friends. Google knows what you are
thinking because it saves your private searches. Facebook can determine your
sexual orientation without you ever mentioning it.” (Schneier,
2015b).
These
effects can be seen in worries surrounding the recording and tracking done
by large companies to follow their customers discussed by
Schneier, (2015a,
2015b)
and others as the crossing of the uncanny valley from useful into dis-
turbing. These examples include the recordings made by a Samsung TV of con-
sumers in their homes (http://www.theguardian.com/media-network/2015/
feb/13/samsungs-listening-tv-tech-rights);
Privacy fears were increased by the
1

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