malice through the looking glass

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Malice Through the Looking Glass Behavior Analysis for the Next Decade Jeff Debrosse

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Page 1: Malice through the looking glass

Malice Through the Looking Glass

Behavior Analysis for the Next Decade

Jeff Debrosse

Page 2: Malice through the looking glass

“It is better to be roughly right than precisely wrong.”

•John Maynard Keynes

Page 3: Malice through the looking glass

ANALYZE THIS…

Industry core focus

• code analysis

• Parse textual content

Page 4: Malice through the looking glass

ANALYZE THAT…

Add social engineering analysis to threat analysis

• Examine the behavior of the victim (underlying causes)

• Treat the disease as well as the symptom(s)!

Page 5: Malice through the looking glass

TRADITIONAL SECURITY DILEMMA

Security Convenience

Page 6: Malice through the looking glass

PSYCHOLOGY AND DECEPTION

“Psychological manipulation of an individual or set of individuals to produce a desired effect on their behavior.“

Page 7: Malice through the looking glass

TODAY’S AV VENDOR GOAL

To increase the security of our customers

• Heuristic Technology

• Cloud-based Solutions

• Others

Today we mostly look for:

• Known bad objects (blacklisting)

• Known good objects (whitelisting, change detection)

Page 8: Malice through the looking glass

THE PSYCHOLOGY OF DETECTION

What does behavior analysis have to do with social engineering?

• Fake AV sells

• Manual analysis = large overhead (and it’s getting larger)

• User behavior: another security layer?

Page 9: Malice through the looking glass

THE PSYCHOLOGY OF DETECTION

JDLR: Cop Talk for “Just Don’t Look Right”

At this point, we may identify software as:

• Already classified

• Resembles badware (JDLR)

• Shares characteristics of badware

• Something which may be good or bad, but has proscribed characteristics

Page 10: Malice through the looking glass

THE HUMAN ELEMENT

“No matter how low an opinion you have of your users, they will

find a way to disappoint you”

• Stamos’ Law (or his corollary to Murphy’s Law)

• Stamos, BH 2009

Page 11: Malice through the looking glass

PROBABILITY AND EMAIL

Bayesian spam filtering

• Counts number of incorrect classifications.

• Low computational overhead

• Very fast machine learning

Page 12: Malice through the looking glass

BAYESIAN ANALYSIS IN ACTION

the phrase “male enhancement” is detected in the body of the email (85% probability of the message being spam)

the subject contains the phrase “real prescription meds” (95% probability)

the body also contains the word (FREE) in all caps (98% probability)

the sender’s email address and sending server are different –99.9% probability)

Page 13: Malice through the looking glass

PROBABILITY AND PEOPLE

Can we predict human behavior (with any accuracy)?

Behavioral targeting does this today!

Page 14: Malice through the looking glass

GET YOUR GAME (THEORY) ON

Game theory attempts to predict behavior such as:

• the interaction between two people

• movements of financial markets

• modern-day warfare

Page 15: Malice through the looking glass

THE PRISONER’S DILEMMA (OR PREDICTABLE RATIONALITY)

S1

confess don’t

S2

confess 10,10 0,20

don’t 20,0 1,1

Page 16: Malice through the looking glass

CONCLUSION

Feedback

Ethics

Optimized by…

• Cloud?

• Aggregation?

• Behavioral Data?Have we reached the

industry’s limits?

Page 17: Malice through the looking glass

QUESTIONS?