Introduction to Biometrics

1. Introduction to Biometrics Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #2 Information Security August 24, 2005

2. Outline • Operating Systems Security • Network Security • Designing and Evaluating Systems • Web Security • Other Security Technologies • Data and Applications Security

3. 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

4. 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\

5. 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

6. 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, - - -

7. 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

8. 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

9. Security Threats to Web/E-commerce

10. 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

11. 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 service • Database transactions are needed for multiuser access to web databases • Need to enforce concurrency control and recovery techniques

12. 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

13. 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

14. Other Security Technologies • Data and Applications Security • Middleware Security • Insider Threat Analysis • Risk Management • Trust and Economics • Biometrics

15. 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

16. 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 Products • 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 management

17. 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

18. 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?

19. 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 Control • 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,

20. SECURITY P R I V A C Y Logic, Proof and Trust Rules/Query Other Services RDF, Ontologies XML, XML Schemas URI, UNICODE Layered Architecture for Dependable Semantic Web • Adapted from Tim Berners Lee’s description of the Semantic Web • Some Challenges: Security and Privacy cut across all layers; Integration of Services; Composability

21. Secure Sensor Information Management: Directions for Research • Individual sensors may be compromised and attacked; need techniques for detecting, managing and recovering from such attacks • Aggregated sensor data may be sensitive; need secure storage sites for aggregated data; variation of the inference and aggregation problem? • 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

22. 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

23. Secure Dependable Information Management Example: Next Generation AWACS Technology provided by the project Navigation Display Consoles Data Analysis Programming Processor Data Links (14) Group (DAPG) & Sensors Refresh Channels Sensor Multi-Sensor • Security being considered after • the system has been designed • and prototypes implemented • Challenge: Integrating real-time • processing, security and • fault tolerance Detections Tracks Future Future Future App App App Data MSI Mgmt. App Data Xchg. Infrastructure Services Real-time Operating System Hardware

24. 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

25. 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.

26. 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

27. Inference Problem as a Privacy Problem: Privacy Constraint Processing User Interface Manager Privacy Constraints Constraint Manager Database Design Tool Constraints during database design operation Update Processor: Constraints during update operation Query Processor: Constraints during query and release operations DBMS Database

28. Secure Data Sharing Across Coalitions Data/Policy for Coalition 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