AI (Artificial Intelligence) is pervading and affecting several domains. What is AI, Machine Learning, Deep Neural Nets and how do we understand them,,

1. AI & SECURITY
Anurag Sahay
Deep Learning, COE, Nagarro

2. ALL THINGS START WITH
PHILOSOPHY

3. Philosophically Fundamental Questions
Can a machine act
intelligently? Can it
solve any problem that
a person would solve
by thinking?
01
Are human intelligence
and machine
intelligence the same?
Is the human
brain essentially a
computer?
02
Can a machine have
a mind, mental states,
and consciousness in
the same way that a
human being can? Can
it feel how things are?
03

4. AI
impact on
Humanity

5. SOME BUSINESS CONTEXT

6. AI is the next digital frontier
In 2016 Companies invested
In artificial Intelligence
Tech Giants Startups
AI Adopters - 20%
in multiple technology areas
AI Partial Adopters - 40%
skeptical about Business Cases and ROI
Laggards - 40%
contemplators

7. Areas where AI creates significant Value
Smarter R&D
and
Forecasting
1
Optimized
Production and
Maintenance
2
Targeted Sales
and Marketing
3
Enhanced User
Experience
4

8. AI – THE BIG PICTURE

9. DEEP
LEARNING
AI – THE SCIENCE
MACHINE
LEARNING
STATISTICAL
SCIENCES
Maths Maths
Algorithms
Maths
Algorithms
Neural Net

10. AI – THE ENGINEERING
MODEL
WORKFLOW
INTEGRATION
MODEL
BUILDING
DATA
PROCESSING
Data Outcome Process

11. AI - USE CASE
PRESCRIPTIVE
W INTELLIGENCE
PREIDICTIVE
INTELLIGENCE
DESCRIPTIVE
INTELLIGENCE
Structure Prediction Action

12. AI – ORGANIZATIONAL MATURITY
AI BASED
ACTION ACTIONS
INSIGHTS
ORIENTED
DATA
BELIEF
Infrastructure Competency Product

13. AI – VARIOUS APPROACHES
FULLY CLOUD
BASED
Cloud Data APIs,
Model APIs,
Workflow APIs
SEMI CLOUD
BASED
Cloud Data APIs
Custom Model
Custom
Workflow
NON CLOUD
Custom Data APIs
Custom Model
Custom Workflow

14. SUPERVISED
MACHINE LEARNING
Learning with Examples!

15. THE LEARNING SPECTRUM
Human Beings learn from experience
We abstract our learning into a rule based
model which we then encode
algorithmically to program a machine
What if we could build a system that could
learn from DATA
Learning from experience Instructions
A system that learns from DATA

16. LINEAR REGRESSION
A simple way to learn from Data
Finding the right line is the important
problem
We solve it using the Gradient Descent
$100K
$500K

17. POLYNOMIAL REGRESSION
Linear Regression is more powerful than it
obviously seems. We can fit a lot of
different types of curves..

18. BAYES
Another interesting approach to
predicting the unknown is through the use
of Bayes Algorithm
cheap
If an email contains the word ‘cheap’ what is the
Probability that it is SPAM ?
3/4
SPAM HAM
Spelling mistake
Missing title

19. DECISION TREE
Decision Trees are a very simple and
intuitive way to solve a lot of different types
of problems

20. LOGISTIC REGRESSION
Use ‘misclassification errors’ instead of
‘distance’ to make a line that separates
two sets of data points

21. NEURAL NETWORK
Multiple ’decision lines’ that can help
separate the data

22. SUPPORT VECTOR
MACHINE
Instead of ‘gradient Descent’ use ‘Linear
Optimization’ to find the line that best
separates the two data sets

23. UNSUPERVISED
MACHINE LEARNING
Examples not included!

24. CLUSTERING
Find ‘ Cluster Structure’ in the Data

25. OUTLIER DETECTION
Find ‘ outliers’ in the Data

26. SECURITY AND AI

27. MALICIOUS URLS
Given a website ‘W’ and a list of
malicious/benign websites , identify
whether ‘W’ is malicious or not
Training Data = URLs of
benign/malicious sites
Use Whitelisted/Blacklisted Websites
URLs from well known sources
http://www.bfuduuioo1fp.mobi/ws/ebayisapi.dll
WHOIS registration 3/25/2008
Hosted from 208.76.89.91/22
IP hosted in Jaipur
Connection Speed T1
Has DNS PTR Record ? Yes
Registrant ”Anurag”
[ 0.56, 9.45, ……. 0 0 1 1 1 …. 1 0 .. 1 0 ]Feature
Building
Real
Valued
Host Based NLP Based
+
Feature Extraction from Website
Website DOM Structure
Advertising Categories
In/Out Links Type
Images on the Website
Now apply, one of the algorithms that we talked abo
and given a new website ‘W’ one can identify whethe
its malicious or not..

28. MALWARE DETECTION
Given a program code, identify whether
it is a malware/benign with some form
(virus, trojan, etc. )
How AV used to detect malware
Pattern matching on Static Files
Use techniques like emulation to decrypt code
How malwares evade AV
Polymorphic Malware metamorphoses to evade
signature identification
Why Machine Learning for Malware Detection
Too many malware/bots/trojan types
The malware code is very contextual
Mobiles, Networks, Devices..
Very processing heavy
AI oriented malware as well
Approach
Use multiple types of features to identify
Static and Behavioral
Exploit Context (Process, Runtime..)
Use layers of ML classifiers to increase confidence
Combine Supervised and Un-Supervised Learning

29. INTRUSION DETECTION
The presence of a ‘threat’ or a ‘risk’
within a system
Attacks can be ”Host based” or
“Network Based”
How we typically detect intrusions?
Look for signatures of known attacks, malicious activ
How threats evade IDS
Signature adaptation, new personalities etc.
Why Machine Learning for IDS
Can detect point intrusions, contextual intrusions and
even collective intrusions. This dramatically broadens
scope of different kinds of threat identification
Approach
Use Anomaly Detection which works on the idea
that the machine learns ‘what is normal’ and if there
is a deviation from the ‘normal’ an ‘attack’ is presume
‘What is Normal’ can be both spatial and temporal an
so quite sophisticated attacks can be easily thwarted

30. AI & SECURITY
AI enables better Security
Security enables better AI
Enforces Integrity
Enforces Privacy
Prevents Misuse
AI security