Example Data Mining for the NBA by leader6


									Introduction to Biometrics

       Dr. Bhavani Thuraisingham
     The University of Texas at Dallas

                Lecture #2
           Information Security

             August 24, 2005
  Operating Systems Security
  Network Security
  Designing and Evaluating Systems
  Web Security
  Other Security Technologies
  Data and Applications Security
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
  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
    - Over time the bits could contain sensitive data
Network Security
  Security across all network layers
    - E.g., Data Link, Transport, Session, Presentation,
  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, - - -
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
  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

                                       Threats and

       Control                                       Fraud          Sabotage
       Violations         Violations

         Denial of                                           Authentication
         Service/                                            Nonrepudiation
         Infrastructure                                      Violations
Approaches and Solutions
   End-to-end security
      - Need to secure the clients, servers, networks, operating
        systems, transactions, data, and programming languages
      - The various systems when put together have to be secure
             Composable properties for security
   Access control rules, enforce security policies, auditing,
    intrusion detection
   Verification and validation
   Security solutions proposed by W3C and OMG
   Java Security
   Firewalls
   Digital signatures and Message Digests, Cryptography
E-Commerce Transactions
  E-commerce functions are carried out as transactions
     - Banking and trading on the internet
     - Each data transaction could contain many tasks
  Database transactions may be built on top of the data transaction
     - Database transactions are needed for multiuser access to web
     - Need to enforce concurrency control and recovery techniques
Types of Transaction Systems
  Stored Account Payment
    - e.g., Credit and debit card transactions
    - Electronic payment systems
    - Examples: First Virtual, CyberCash, Secure Electronic Transaction
  Stored Value Payment
    - Uses bearer certificates
    - Modeled after hard cash
           Goal is to replace hard cash with e-cash
    - Examples: E-cash, Cybercoin, Smart cards
What is E-Cash?
  Electronic Cash is stored in a hardware token
  Token may be loaded with money
     - Digital cash from the bank
  Buyer can make payments to seller’s token (offline)
  Buyer can pay to seller’s bank (online)
  Both cases agree upon protocols
  Both parties may use some sort of cryptographic key mechanism to
   improve security
Other Security Technologies
  Data and Applications Security
  Middleware Security
  Insider Threat Analysis
  Risk Management
  Trust and Economics
  Biometrics
Developments in Data and Applications
Security: 1975 - Present

  Access Control for Systems R and Ingres (mid 1970s)
  Multilevel secure database systems (1980 – present)
     - Relational database systems: research prototypes and products;
       Distributed database systems: research prototypes and some
       operational systems; Object data systems; Inference problem
       and deductive database system; Transactions
  Recent developments in Secure Data Management (1996 – Present)
     - Secure data warehousing, Role-based access control (RBAC); E-
       commerce; XML security and Secure Semantic Web; Data
       mining for intrusion detection and national security; Privacy;
       Dependable data management; Secure knowledge management
       and collaboration
Developments in Data and Applications
Security: Multilevel Secure Databases - I
   Air Force Summer Study in 1982
   Early systems based on Integrity Lock approach
   Systems in the mid to late 1980s, early 90s
      - E.g., Seaview by SRI, Lock Data Views by Honeywell, ASD and
        ASD Views by TRW
      - Prototypes and commercial products
      - Trusted Database Interpretation and Evaluation of Commercial
   Secure Distributed Databases (late 80s to mid 90s)
      - Architectures; Algorithms and Prototype for distributed query
        processing; Simulation of distributed transaction management
        and concurrency control algorithms; Secure federated data
Developments in Data and Applications
Security: Multilevel Secure Databases - II
  Inference Problem (mid 80s to mid 90s)
     - Unsolvability of the inference problem; Security constraint
       processing during query, update and database design
       operations; Semantic models and conceptual structures
  Secure Object Databases and Systems (late 80s to mid 90s)
     - Secure object models; Distributed object systems security;
       Object modeling for designing secure applications; Secure
       multimedia data management
  Secure Transactions (1990s)
     - Single Level/ Multilevel Transactions; Secure recovery and
       commit protocols
Some Directions and Challenges for Data and
Applications Security - I
   Secure semantic web
     - Single/multiple security models?
     - Different application domains
   Secure Information Integration
     - How do you securely integrate numerous and heterogeneous
       data sources on the web and otherwise
   Secure Sensor Information Management
     - Fusing and managing data/information from distributed and
       autonomous sensors
   Secure Dependable Information Management
     - Integrating Security, Real-time Processing and Fault Tolerance
   Data Sharing vs. Privacy
     - Federated database architectures?
Some Directions and Challenges for Data and
Applications Security - II
   Data mining and knowledge discovery for intrusion detection
     - Need realistic models; real-time data mining
   Secure knowledge management
     - Protect the assets and intellectual rights of an organization
   Information assurance, Infrastructure protection, Access
     - Insider cyber-threat analysis, Protecting national databases,
       Role-based access control for emerging applications
   Security for emerging applications
     - Geospatial, Biomedical, E-Commerce, etc.
   Other Directions
     - Trust and Economics, Trust Management/Negotiation, Secure
       Peer-to-peer computing,
Layered Architecture for Dependable
Semantic Web
   0Adapted from Tim Berners Lee’s description of the Semantic Web

     S   P           Logic, Proof and Trust
     E   R
     C   I                Rules/Query
     U   V                                               Other
     R   A                                               Services
     I   C              RDF, Ontologies
     T   Y
     Y              XML, XML Schemas

                           URI, UNICODE

  0 Some Challenges: Security and Privacy cut across all layers;
  Integration of Services; Composability
Secure Sensor Information Management:
Directions for Research
  Individual sensors may be compromised and attacked; need
   techniques for detecting, managing and recovering from such
  Aggregated sensor data may be sensitive; need secure storage sites
   for aggregated data; variation of the inference and aggregation
  Security has to be incorporated into sensor database management
     - Policies, models, architectures, queries, etc.
  Evaluate costs for incorporating security especially when the sensor
   data has to be fused, aggregated and perhaps mined in real-time
  Need secure dependable information management for sensor data
Secure Dependable Information Management
  Dependable information management includes
     - secure information management
     - fault tolerant information
     - High integrity and high assurance computing
     - Real-time computing
  Conflicts between different features
     - Security, Integrity, Fault Tolerance, Real-time Processing
     - E.g., A process may miss real-time deadlines when access
       control checks are made
     - Trade-offs between real-time processing and security
     - Need flexible security policies; real-time processing may be
       critical during a mission while security may be critical during
       non-operational times
Secure Dependable Information Management
Example: Next Generation AWACS

                                Data Analysis Programming                      Display           Consoles
         Data Links                                                           Processor            (14)
                                      Group (DAPG)                                &
         Sensors                                                               Refresh

                    Sensor              Multi-Sensor
                   Detections             Tracks                             •Security being considered after
Technology                                                                   the system has been designed
                                                Future     Future   Future
provided by                                      App        App      App     and prototypes implemented
the project
                                                                             •Challenge: Integrating real-time
                       Data              MSI                                 processing, security and
                       Mgmt.    Data
                                         App                                 fault tolerance
                                 Infrastructure Services

                                Real-time Operating System

Research Directions for Privacy
   Why this interest now on privacy?
     -   Data Mining for National Security
     -   Data Mining is a threat to privacy
     -   Balance between data sharing/mining and privacy
   Privacy Preserving Data Mining
   Inference Problem as a Privacy Problem
   Data Sharing Across Coalitions
Data Mining to Handle Security Problems
  Data mining tools could be used to examine audit data and flag
   abnormal behavior
  Much recent work in Intrusion detection
     - e.g., Neural networks to detect abnormal patterns
  Tools are being examined to determine abnormal patterns for
   national security
     - Classification techniques, Link analysis
  Fraud detection
     - Credit cards, calling cards, identity theft etc.
What can we do?:
Privacy Preserving Data Mining
  Prevent useful results from mining
     - limit data access to ensure low confidence and support
     - Extra data (“cover stories”) to give “false” results with Providing
       only samples of data can lower confidence in mining results;
  Idea: If adversary is unable to learn a good classifier from the data,
   then adversary will be unable to learn good
     - rules, predictive functions
  Approach: Only make a sample of data available
     - Limits ability to learn good classifier
  Several recent research efforts have been reported
Inference Problem as a Privacy Problem:
 Privacy Constraint Processing

                User Interface Manager

       Privacy            Constraint                      Database Design
       Constraints        Manager                         Tool
                                                          Constraints during
                                                          database design
             Query Processor:          Update             operation
             Constraints during
             query and release         Constraints
             operations                during update

                           DBMS                        Database
Secure Data Sharing Across Coalitions

                          Data/Policy for Coalition

     Export                                            Export
     Data/Policy                                       Data/Policy

        Component                                         Component
        Data/Policy for                                   Data/Policy for
        Agency A                                          Agency C

                                     Data/Policy for
                                     Agency B

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