1. Data and Applications Security Developments and Directions
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Lecture #2
Supporting Technologies
August 26, 2009

2. Objective of the Unit
This unit will provide an overview of the supporting technologies

3. Outline of Part I: Information Security
Operating Systems Security
Network Security
Designing and Evaluating Systems
Web Security
Other Security Technologies

4. Operating System Security
Access Control
Subjects are Processes and Objects are Files
Subjects have Read/Write Access to Objects
E.g., Process P1 has read acces to File F1 and write access to File F2
Capabilities
Processes must presses certain Capabilities / Certificates to access certain files to execute certain programs
E.g., Process P1 must have capability C to read file F

5. Mandatory Security Bell and La Padula Security Policy
Subjects have clearance levels, Objects have sensitivity levels; clearance and sensitivity levels are also called security levels
Unclassified < Confidential < Secret < TopSecret
Compartments are also possible
Compartments and Security levels form a partially ordered lattice
Security Properties
Simple Security Property: Subject has READ access to an object of the subject’s security level dominates that of the objects
Star (*) Property: Subject has WRITE access to an object if the subject’s security level is dominated by that of the objects\

6. Covert Channel Example
Trojan horse at a higher level covertly passes data to a Trojan horse at a lower level
Example:
File Lock/Unlock problem
Processes at Secret and Unclassified levels collude with one another
When the Secret process lock a file and the Unclassified process finds the file locked, a 1 bit is passed covertly
When the Secret process unlocks the file and the Unclassified process finds it unlocked, a 1 bit is passed covertly
Over time the bits could contain sensitive data

7. Network Security Security across all network layers
E.g., Data Link, Transport, Session, Presentation, Application
Network protocol security
Ver5ification and validation of network protocols
Intrusion detection and prevention
Applying data mining techniques
Encryption and Cryptography
Access control and trust policies
Other Measures
Prevention from denial of service, Secure routing, - - -

8. Data Security: Access Control
Access Control policies were developed initially for file systems
E.g., Read/write policies for files
Access control in databases started with the work in System R and Ingres Projects
Access Control rules were defined for databases, relations, tuples, attributes and elements
SQL and QUEL languages were extended
GRANT and REVOKE Statements
Read access on EMP to User group A Where EMP.Salary < 30K and EMP.Dept <> Security
Query Modification:
Modify the query according to the access control rules
Retrieve all employee information where salary < 30K and Dept is not Security

9. Steps to Designing a Secure System
Requirements, Informal Policy and model
Formal security policy and model
Security architecture
Identify security critical components; these components must be trusted
Design of the system
Verification and Validation

10. Product Evaluation Orange Book
Trusted Computer Systems Evaluation Criteria
Classes C1, C2, B1, B2, B3, A1 and beyond
C1 is the lowest level and A1 the highest level of assurance
Formal methods are needed for A1 systems
Interpretations of the Orange book for Networks (Trusted Network Interpretation) and Databases (Trusted Database Interpretation)
Several companion documents
Auditing, Inference and Aggregation, etc.
Many products are now evaluated using the federal Criteria

11. Security Threats to Web/E-commerce

12. Other Security Technologies
Middleware Security
Insider Threat Analysis
Risk Management
Trust and Economics
Biometrics
Secure Voting Machines

13. Outline of Part II: Data Management
Concepts in database systems
Types of database systems
Distributed Data Management
Heterogeneous database integration
Federated data management

14. An Example Database System
Adapted from C. J. Date, Addison Wesley, 1990

15. Metadata Metadata describes the data in the database
Example: Database D consists of a relation EMP with attributes SS#, Name, and Salary
Metadatabase stores the metadata
Could be physically stored with the database
Metadatabase may also store constraints and administrative information
Metadata is also referred to as the schema or data dictionary

16. Functional Architecture
Data Management
User Interface Manager
Schema
(Data Dictionary)
Manager
(metadata)
Query
Manager
Security/
Integrity
Manager
Transaction Manager
Storage Management
File
Manager
Disk
Manager

17. DBMS Design Issues Query Processing Optimization techniques
Transaction Management
Techniques for concurrency control and recovery
Metadata Management
Techniques for querying and updating the metadatabase
Security/Integrity Maintenance
Techniques for processing integrity constraints and enforcing access control rules
Storage management
Access methods and index strategies for efficient access to the database

18. Types of Database Systems
Relational Database Systems
Object Database Systems
Deductive Database Systems
Other
Real-time, Secure, Parallel, Scientific, Temporal, Wireless, Functional, Entity-Relationship, Sensor/Stream Database Systems, etc.

19. Relational Database: Example
Relation S:
S# SNAME STATUS CITY
S1 Smith London
S2 Jones Paris
S3 Blake Paris
S4 Clark London
S5 Adams Athens
Relation P:
P# PNAME COLOR WEIGHT CITY
P1 Nut Red London
P2 Bolt Green Paris
P3 Screw Blue Rome
P4 Screw Red London
P5 Cam Blue Paris
P6 Cog Red London
Relation SP:
S# P# QTY
S1 P
S1 P
S1 P
S1 P
S1 P
S1 P
S2 P
S2 P
S3 P
S4 P
S4 P
S4 P

20. Example Class HierarchyID
Name
Author
Publisher
Document
Class D1 D2
Method1:
Method2:
Print-doc-att(ID)
Print-doc(ID)
Journal
Subclass
Book Subclass
# of Chapters
Volume # B1 J1

21. Example Composite Object
Document
Object
Section 2
Object
Section 1
Object
Paragraph 1
Object
Paragraph 2
Object

22. Distributed Database System
Communication Network
Distributed
Processor 1
DBMS 1
Data-
base 1
base 3
base 2
DBMS 2
DBMS 3
Processor 2
Processor 3
Site 1
Site 2
Site 3

23. Data Distribution S I T E 1 E M P 1 D E P T 1 S S # N a m e S a l a ry D # D # D n a m e M G R 1 J o h n 2 1 1 C . S c i . J a n e 2 P a u l 3 2 3 J a m e s 4 2 3 E n g l i s h D a v i d 4 J i l l 5 2 5 M a r y 6 1 4 F r e n c h P e t e r 6 J a n e 7 2 S I T E 2 E M P 2 D E P T 2 S S # N a m e S a l a r y D # D # D n a m e M G R 9 M a t h e w 7 5 5 M a t h J o h n 7 D a v i d 8 3 P h y s i c s P a u l 8 P e t e r 9 4 2

24. Interoperability of Heterogeneous Database Systems
Database System A
Database System B
(Relational)
(Object-
Oriented)
Network
Transparent access
to heterogeneous
databases -
both users
and application
programs;
Query, Transaction
processing
Database System C
(Legacy)

25. Different Data Models Network Node A Node B Node C Node D
Database
Database
Database
Database
Relational
Model
Network
Model
Hierarchical
Model
Object-
Oriented Model
Developments: Tools for interoperability; commercial products
Challenges: Global data model

26. Federated Database Management
Database System A
Database System B
Federation F1
Cooperating database
systems yet maintaining
some degree of
autonomy
Federation F2
Database System C

27. Federated Data and Policy Management
Data/Policy for Federation
Export
Export
Data/Policy
Data/Policy
Export
Data/Policy
Component
Component
Data/Policy for
Data/Policy for
Agency A
Agency C
Component
Data/Policy for
Agency B

28. Outline of Part I: Information Management
Information Management Framework
Information Management Overview
Some Information Management Technologies
Knowledge Management

29. What is Information Management?
Information management essentially analyzes the data and makes sense out of the data
Several technologies have to work together for effective information management
Data Warehousing: Extracting relevant data and putting this data into a repository for analysis
Data Mining: Extracting information from the data previously unknown
Multimedia: managing different media including text, images, video and audio
Web: managing the databases and libraries on the web

30. Data Warehouse Users Query the Warehouse Data Warehouse:
Data correlating
Employees With
Medical Benefits
and Projects
Could be
any DBMS;
Usually based on
the relational
data model
Oracle
DBMS for
Employees
Sybase
DBMS for
Projects
Informix
DBMS for
Medical

31. Data Mining Information Harvesting Knowledge Mining Data Mining
Knowledge Discovery
in Databases
Data Archaeology
Data Dredging
Database Mining
Knowledge Extraction
Data Pattern Processing
Information Harvesting
Siftware
The process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data, often previously unknown, using pattern recognition technologies and statistical and mathematical techniques
(Thuraisingham 1998)

32. Multimedia Information Management
Broadcast News Editor (BNE)
Video
Source
Broadcast News
Navigator (BNN)
Scene
Change
Detection
Correlation
Story
GIST Theme
Broadcast
Detection
Frame
Classifier
Commercial
Detection
Key Frame
Selection
Imagery
Silence
Detection
Story
Segmentation
Multimedia
Database
Management
System
Audio
Speaker
Change
Detection
Closed
Caption
Text
Token
Detection
Video
and
Metadata
Closed
Caption
Preprocess
Named
Entity
Tagging
Segregate
Video
Streams
Analyze and Store Video and Metadata
Web-based Search/Browse by
Program, Person, Location, ...

33. Image Processing: Example: Change Detection:
Trained Neural Network to predict “new” pixel from “old” pixel
Neural Networks good for multidimensional continuous data
Multiple nets gives range of “expected values”
Identified pixels where actual value substantially outside range of expected values
Anomaly if three or more bands (of seven) out of range
Identified groups of anomalous pixels
Started with two known anomalies (ship, circle) -- third (parking lot) detected by algorithm.
Neural net takes 7 bands of old as input, 7 bands of new as output.
Multiple nets trained, gives range of predictions. Width of range gives measure of how “confident” the prediction is.
Measure ratio of actual-predicted to range, if high is anomaly
High defined as 4 standard deviations above average ratio for that band
Anomalous pixel if 3 or more anomalous bands
Anomalous region if 6 or more bad pixels in 3x3 block.

34. Semantic Web
Adapted from Tim Berners Lee’s description of the Semantic Web
XML, XML Schemas
Rules/Query
Logic, Proof and Trust
TRUST
Other
Services
RDF, Ontologies
URI, UNICODE P R I V A C Y
Some Challenges: Security and Privacy cut across all layers

35. Knowledge Management Components
Components of
Management:
Components,
Cycle and
Technologies
Cycle:
Technologies:
Components:
Knowledge, Creation
Expert systems
Strategies
Sharing, Measurement
Collaboration
Processes
And Improvement
Training
None of these things were endorsed by military acquisitions, but all have gradually started happening out of necessity and user requirements.
Metrics
Web