This document discusses the importance of context over just content or data in digital evidence analysis. It argues that context allows one to derive meaning, linkages, intentions, and knowledge from data by considering things like relationships, environment, narratives and what is absent. Visualization tools are important to help analyze patterns and connections in the data. The goal is to interpret data in a way that provides insights, decisions and statistical analysis, rather than just presenting facts.

1. Digital Evidence Analytics:
What does the evidence
really mean?
The 2010 ADFSL Conference on
Digital Forensics, Security and Law
May 19-21, 2010
St. Paul, Minnesota, USA
Tuesday, May 25, 2010 1

2. Dr. Marcus K. Rogers
University Faculty Scholar
Fellow of CERIAS
Director - Cyber Forensics Program
College of Technology
Purdue University
CERIAS
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3. DE evolution
Acquisition Focused
All about the data!
Examination and Analysis
Information is King!
Interpretation
Knowledge??
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4. context
• How do we get there from here?
• Content is not the be all, live all, end
all!
• What meaning can we ascribe to
what we are seeing?
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5. context v. content
• allows for attributions to be attached to
the data.
• relational and/or structure and meaning to
the data.
• determines the value or weight of the raw
data.
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6. context v. content
• totality of the physical and electronic/virtual
environment.
• what is missing or absent can be as important as
what is there (e.g., missing log files, wiped data
areas).
• personal narrative is the key to connecting the data
points and more importantly, predicting future
behavior (of either the system or the user).
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7. what can the data tell us?
• Context
• Meaning
• Personal Narrative
• Linkages
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8. what can the data tell us?
• Intentions of individual or group (past & future)
• Social networks
• Technical capacity
• Resources
• Organizational structure
• Organizational activities
• Environment
• Pattern of life
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9. connecting the dots
• Pattern analysis
• Chronologies (e.g., timelines)
• Frequency analyses
• Hierarchical connections or nodes
• small world networks - (degrees of
separation in social networks), dense
connection nodes
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10. visualization
• Graphical representations allow for better initial analysis
by humans (non machine learning systems)
• Heatmaps
• color coded to indicate relationships and
importance
• Dashboard or console UI's.
• Allow quick summary with the ability to drill down
to various levels of granularity
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11. visualization
• Timelines
• using drill down charts that can be
superimposed over other interfaces
• Mind maps
• dynamic fluid relationships and
interconnections at different levels of
granularity
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12. points of view
• investigators v. analysts
• technical v. analytical
• our frame of reference is vital
• communication is vital
• asking better questions of the data!
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13. analysis
Scientific Investigative Analytics
Method
Theory who Data driven
development what (data mining)
when Decision making
Hypothesis
testing
where Statistical
why analysis
Probabilities how Pattern
Error rates identification
Accuracy
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14. Summary
• It is not all about the data...its not all about
the information.
• Information consists of facts and data organized to
describe a particular situation or condition.
• It is really about the knowledge!
• Knowledge is applied to interpret information about the
situation and to decide how to handle it.
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15. “There is nothing more
deceptive than an obvious
fact”
Sir Arthur Conan Doyle
Sherlock Holmes
The Boscombe Valley Mystery
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16. contact information
Dr. Marcus Rogers
765-494-2561
cyberforensics@mac.com
http://cyberforensics.purdue.edu
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