1. STEGANOGRAPHY: Data Mining:
SOUNDARARAJAN EZEKIEL
Department of Computer Science
Indiana University of Pennsylvania
Indiana, PA 15705

2. Steganography Cryptography Data Mining
Art of hiding information
in ways that prevent the detection of hidden message
Existence is not know
Science of writing in secret code
It encodes a message so it cannot be understood
Discovering hidden
Values in your data
Warehouse
That is
The extraction of hidden predictive information from large database
Knowledge discovery method– extraction of implicit and interesting pattern from large data collection

3. Data Mining-- Introduction
It started when we started to store data in computer( businesses)
Continued improvements– technology that navigate through data in real time
Examples:-
Single case:
Web server collect data for every single cleick
Logs are too big and contain gibberish
Lots of data and statistics
What we collected is not really useful
Multiple Case:-
Collection of web servers with large bandwidth
Think about the size of the data we collect

4. Data Mining --- Continue
It helps to design better and more intelligent business( e-learning environments) because it supported by
Massive data collection
Powerful multiprocessor computers
Good data mining algorithms
It existed at least 10 years, but it is getting popular recently
Example:-
Winter Corporation Report
Data warehouses with as much as 100 to 200 terabytes of raw data will be operational by next year, performing nearly 2,000 concurrent queries and occupying nearly 1 petabyte (1,000 terabytes) of disk space. In the same time period, transaction-processing databases will handle workloads of nearly 66,000 transactions per second

5. Evolution of Data mining
Evolutionary step
Question
Tech
Product
providers
characteristics
Data collection
60’s
What was my total revenue last few years
Computer, tapes, disks
IBM , CDC
Retrospective static data delivery
Data Access
80’s
What were unit sales in India last year January
RDBMS(Relational DataBases)
SQL( Structured Query Languages)
ODBC
Oracle
Sybase
Informix
IBM
Microsoft
Dynamic data delivery
Data warehouse and decision support
90’s
What were unit sales price in India last March?
On-line analytic processing (OLAP)
Multidimensional data base, data warehouses
Pilot
Comshare
Arbor
Cognos
Microstrategy
Dynamic data delivery in multiple level
Data mining
Now
What will be unit price in India next month?
Why?
Advanced algorithms, multiprocessor computers, massive database
Lockheed
IBM,SGI
Many more…
Prospective, proactive information delivery

6. The scope of Data mining
It is similar to sifting gold from immense amount of dirt--- searching valuable information in a gigabytes data
Automated prediction of trends and behaviors: Data mining automates the process of finding predictive information in a large database.
Example: Question related to target marketing
Data mining can use mailing list data– other previous data to identify the solution
Another example- Forecasting bankruptcy by identifying segments of a population likely to respond similarly to given events

7. Data base can be larger in both depth and breadth
Automated discovery of previously unknown patterns: It sweep through the database and identify previously hidden patterns in one step
Example: Unrelated items purchased together in a store.
Detecting fraudulent credit card transactions etc
Data base can be larger in both depth and breadth
High performance data mining need to analyze full depth of a database without pre-selecting subsets
Larger samples yield lower estimation errors and variances

8. Research Rank
2001 – According to MIT’s Technology Review – Data mining is a top 10 research area
Recently – According to Gartner Group Advanced Technology Research Note– data mining and AI is top 5 key research area.

9. Multi-disciplinary field with a broad applicability
Has several applications
Market based analysis
Customer relationship management
Fraud detection
Network intrusion detection
Non-destructive eavaluation
Astronomy (look up dataa)
Remote sensing data
( look down data)
Text and mulitmedia mining
Medical imaging
Automated target recognition
Combined ideas from several diffferent fields
Steganography-- Cryptography
My point of view of Data mining
Borrowing the idea from
Machine Learning
Artificial Intelligence
Statistics
High performance computing
Signal and Image Processing
Mathematical Optimization
Pattern Recognition
Natural Language processing
Steganography
Cryptography

10. General view of Data mining
Preprocessed
data
Raw
Data
Target
Data
Knowledge
Transformed
Data
Pattern
Data processing pattern recog. Interpreting results
Dimension
Reduction
Data Fusion
Sampling
MRA
De-noising
Object Identification
Feature Extraction
Normalization
Classification
Clustering
Regression
Visualization
Validation
An Iterative and Interactive Process

11. Our Research Based On Data Preprocessing Pattern Recognition
Multiresolution Analysis
De-noising ( wavelet based methods)
Object Classifications
Feature Extraction
Pattern Recognition
Classification
Clustering
Visualization and Validation
Steganography
Cryptography

12. Where we are going from here
More robust , accurate, scalable algorthim
For pre-processing and pattern recognition
Wavelets– and fractals
Newer data types
Video and multimedia
Multi-sensor data
More complex problems
Dynamic tracking in video
Mining text, audio, video, images
Investigating Steganography in images, analysis of data hiding methods, attacks against hidden information, and counter measures to attacks against digital watermarking ( detection and distortion)

13. How data mining works?
How exactly the data mining able to tell you important things that you did not know or what is going to happen next?
The method/ techniques that is used to perform these feats in data mining is called modeling
Modeling is simply the act of building a model in one situation where you know the answer and then applying it to another situation that you don’t
Example: Sunken treasure ship– Bermuda shore, other ships– path-- keep all these information– build the model– if the model is good– you find the treasure in the ocean
Example 2: Identify telephone customer– for example you have the information that is the model that 98% customer who makes $60K per year spend more than $80 per month on long distance
with this model new customer can be selectively targeted

14. Most commonly used techniques
Artificial Neural Networks: Non linear predictive models that learn through training and resemble biological neural networks in structure
Decision Trees: Tree- shaped structures that represents set of decisions . These decisions generated rules for the classification of a dataset. Specific decision tree include classification and Regression Test(CART)and Chi Square Automated Interaction Detection (CAID)
Genetic Algorithms: optimization techniques that uses processes genetic combination, mutation, and selection in a design based on the concept of evolution
Nearest Neighbor Method:
Rule Induction:
OUR METHODS WILL BE BASED ON WAVELETS, FRACTALS, STEG, AND CRYPT

15. Steganography Methods
Lets us discuss few methods and its advantage and disadvantage
1. Least Significant Method
Idea:- Hide the hidden message in LSB of the pixels
Example:-
Advantage:- quick and easy– works well in gray image
Disadvantage:- insert in 8 bit– changes color– noticeable change– vulnerable to image processing– cropping and compression

16. STEGANALYSIS Detection Distortion
Redundant method
Store more than one time--- withstand cropping
Spread Spectrum
Store the hidden message everywhere
STEGANALYSIS
Detection Distortion
Analyst manipulate the stego-media
To render the embedded information
Useless or remove it altogether
Analyst observe various
Various relationship between
Cover, message, stego-media
Steganography tool
Seeing the Unseen

17. DCT - Discrete Cosine Transformation
Encode
Take image
Divide into 8x8 blocks
Apply 2-D DCT--- DCT coefficients
Apply threshold value
Store the hidden message in that place
Take inverse– store as image
Decode
Start with modified image
Apply DCT
Find coefficient less than T
Extract bits
Combine bits and make message

18. Wavelets Transformation
Wavelets are basis function in continuous time.
a basis is a set of linearly independent functions that can be used
to produce all admissible functions f(t)
The special feature of wavelet basis is that all functions
are constructed from a single mother wavelet w(t). This wavelet is
is a small wave ( a pulse). Normally it starts at time t=0 and end at
time t=N
Shifted k time =
Compressed =
Combine both we have
Haar Wavelet : Haar, – theory, 88– daubechies Mallat 2-d, mra, bi-orthogonal
Haar=

19. Inverse Transformation Extract the Hidden Message
figure
Carrier
Stego image
Wavelet
Transformation
Thresholding
Compression
Message to be Hidden
Error Image
Inverse Transformation
Extract the Hidden Message

20. Information security and data mining
Goal of intrusion detection – discover intrusion into a computer or network
With internet and available tool for attacking networks– security becomes a critical component of network
Misuse detection: finds intrusion by looking for activity corresponding to known techniques for intrusion
Anomaly detection: the system defines the expected behavior of the network in advance

21. What we want The tools to filter and classify information
Tools to find and retrieve the relevant information when you need it
Tools that adapt to your pace and needs
Tools to predict information needs
Tools to recommend tasks and information sources
Tools than can be personalized, manually or automatically

22. The tools should be… Non- intrusive Secure Integrated Adaptable
Controllable
Automatic or semi-automatic
Useful
For learners
For educators
Integrate operational data with customer, suppliers and market --

23. Profitable application
A wide range of companies have deployed successful application of data mining
Some applications area include
A pharmaceutical company can analyze its recent sales force activity and their results to improve target of high-value physician and determine which marketing activities will have the greatest impact in the next few months
A credit card companies can leverage its vast warehouse of customers transactions data to identify customers most likely to be interested in a new credit product
A diversified transportation company with a large direct sales forces can apply data mining to identify the best prospect for its services
A large consumer package goods company can apply data mining to improve its sales process to retailers

24. Conclusion In this talk, we have discussed data mining related topics
Our goals
Research
Software and algorithms
Application
Our main focus is Science Data, though applicable to other data sets as well
More information – check out website
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