Transcript Lecture 4 - The University of Texas at Dallas

Data and Applications Security
Developments and Directions
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Lecture #4
Supporting Technologies
January 2010
Objective of the Unit
 This unit will provide an overview of the supporting technologies
Outline of Part I: Information Security
 Operating Systems Security
 Network Security
 Designing and Evaluating Systems
 Web Security
 Other Security Technologies
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
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\
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
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, - - -
-
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
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
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
Security Threats to Web/E-commerce
Security
Threats and
Violations
Access
Control
Violations
Denial of
Service/
Infrastructure
Attacks
Integrity
Violations
Fraud
Sabotage
Confidentiality
Authentication
Nonrepudiation
Violations
Other Security Technologies
 Middleware Security
 Insider Threat Analysis
 Risk Management
 Trust and Economics
 Biometrics
 Secure Voting Machines
 -----
Outline of Part II: Data Management
 Concepts in database systems
 Types of database systems
 Distributed Data Management
 Heterogeneous database integration
 Federated data management
An Example Database System
Application
Programs
Database Management System
Database
Adapted from C. J. Date, Addison Wesley, 1990
Users
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
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
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
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.
Relational Database: Example
Relation S:
S#
S1
S2
S3
S4
S5
SNAME
Smith
Jones
Blake
Clark
Adams
Relation SP:
STATUS CITY
20
London
10
Paris
30
Paris
20
London
30
Athens
Relation P:
P#
P1
P2
P3
P4
P5
P6
PNAME
Nut
Bolt
Screw
Screw
Cam
Cog
COLOR WEIGHT CITY
Red
12
London
Green
17
Paris
Blue
17
Rome
Red
14
London
Blue
12
Paris
Red
19
London
S#
S1
S1
S1
S1
S1
S1
S2
S2
S3
S4
S4
S4
P#
P1
P2
P3
P4
P5
P6
P1
P2
P2
P2
P4
P5
QTY
300
200
400
200
100
100
300
400
200
200
300
400
Example Class Hierarchy
Document
Class
D1
D2
ID
Name
Author
Publisher
Method1:
Print-doc-att(ID)
Journal
Book Subclass
B1
Method2:
Print-doc(ID)
Subclass
Volume #
# of Chapters
J1
Example Composite Object
Composite
Document
Object
Section 2
Object
Section 1
Object
Paragraph 1
Object
Paragraph 2
Object
Distributed Database System
Database 1
Database 3
DBMS 3
Distributed
Processor 3
Site 3
DBMS 1
Distributed
Processor 1
Communication Network
Site 1
Database 2
Distributed
Processor 2
DBMS 2
Site 2
Data Distribution
SITE 1
EMP1
DEPT1
SS#
Name
Salary
D#
D#
Dname
MGR
1
2
3
4
5
6
John
Paul
James
Jill
Mary
Jane
20
30
40
50
60
70
10
20
20
20
10
20
10
C. Sci.
Jane
30
English
David
40
French
Peter
D#
DEPT2
Dname
MGR
50
Math
John
20
Physics
Paul
SITE 2
EMP2
SS#
9
Name
Mathew
Salary
70
D#
50
7
David
80
30
8
Peter
90
40
Interoperability of Heterogeneous Database
Systems
Database System A
Database System B
(Relational)
(ObjectOriented)
Network
Transparent access
to heterogeneous
databases both users
and application
programs;
Query, Transaction
processing
Database System C
(Legacy)
Different Data Models
Network
Node A
Node B
Database
Database
Relational
Model
Network
Model
Node C
Database
Hierarchical
Model
Node D
Database
ObjectOriented Model
Developments: Tools for interoperability; commercial products
Challenges:
Global data model
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
Federated Data and Policy Management
Data/Policy for Federation
Export
Data/Policy
Export
Data/Policy
Export
Data/Policy
Component
Data/Policy for
Agency A
Component
Data/Policy for
Agency C
Component
Data/Policy for
Agency B
Outline of Part I: Information Management
 Information Management Framework
 Information Management Overview
 Some Information Management Technologies
 Knowledge Management
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
Data Warehouse
Users
Query
the Warehouse
Oracle
DBMS for
Employees
Data Warehouse:
Data correlating
Employees With
Medical Benefits
and Projects
Sybase
DBMS for
Projects
Could be
any DBMS;
Usually based on
the relational
data model
Informix
DBMS for
Medical
Data Mining
Information Harvesting
Knowledge Mining
Data Mining
Knowledge Discovery
in Databases
Data Dredging
Data Archaeology
Data Pattern Processing
Database Mining
Knowledge Extraction
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)
Multimedia Information Management
Video
Source
Broadcast News Editor (BNE)
Scene
Change
Detection
Frame
Classifier
Imagery
Silence
Detection
Correlation
Story
GIST Theme
Broadcast
Detection
Commercial
Detection
Key Frame
Selection
Story
Segmentation
Audio
Closed
Caption
Text
Speaker
Change
Detection
Closed
Caption
Preprocess
Segregate
Video
Streams
Broadcast News
Navigator (BNN)
Token
Detection
Named
Entity
Tagging
Analyze and Store Video and Metadata
Multimedia
Database
Management
System
Video
and
Metadata
Web-based Search/Browse by
Program, Person, Location, ...
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
Semantic Web
0Adapted from Tim Berners Lee’s description of the Semantic Web
T
R
U
S
T
P
R
I
V
A
C
Y
Logic, Proof and Trust
Rules/Query
RDF, Ontologies
Other
Services
XML, XML Schemas
URI, UNICODE
0 Some Challenges: Security and Privacy cut across all layers
Knowledge Management Components
Knowledge
Components of
Management:
Components,
Cycle and
Technologies
Components:
Strategies
Processes
Metrics
Cycle:
Knowledge, Creation
Sharing, Measurement
And Improvement
Technologies:
Expert systems
Collaboration
Training
Web