1. Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #18 Data Mining for Security Applications March 17, 2005

2. Outline l National Security/Cyber Security : Threats and Countermeasures l Overview of Data Mining l Data Mining for National Security/Cyber Security l Privacy Concerns

3. Aspects of Counterterrorism l National Security Measures l Protection from Non-real-time Threats l Protection from Real-time Threats l Protection from Cyberterrorism - Cyber security l Protection from Bioterrorism l Preventing/Detecting/Containing Terrorist activities

4. Some National Security Measures l Border Security - Protecting National/State Borders - Protecting Information Flows across borders - Managing and Mitigating Risks l First/Emergency Responses - Detecting attacks (cyber or otherwise) and responding to attacks in a timely manner - Containing the damage l Continued Monitoring and Management - Manage attacks, lessons learned and prevent future attacks - Surveillance, vigilance

5. Protection Objects for National Security l Services - Transportation, Financial, Medical, Insurance, Education, - - - l Infrastructures - Telecommunication networks, Power systems, water supply/tanks/reservoirs l Information related - Computing systems and networks, National databases, Financial databases., Medical databases, - - -

6. Cyber Security Measures l Protection from Trojan Horses and Viruses l Protection from Jamming l Recovering from network and system failures through malicious attacks l Intrusion detection/prevention, auditing l Secure clients, secure servers, secure networks and protocols, secure middleware l Develop and enforce security policies for Intranets and Internet l Secure collaboration, e-commerce, transactions l Access control, identification, authentication, nonrepudiation

7. National Security vs Cyber Security l Cannot separate the two; Much of the data is now or will be on the web l Digital libraries have emerged; semantic web is a matter of time l Example: Border security measures include physically protecting the borders as well as protecting information flow across borders l Transportation security l Effective cyber security measures could prevent national security disasters - e.g., monitoring email and data on the web

8. Some common threads l Identify threats and group/classify threats l Learn from experiences, prior situations l Develop techniques to prevent attacks l Develop techniques to detect attacks, deal with attacks in a timely manner l Develop techniques to monitor and prevent future attacks

9. What is Data Mining? Data MiningKnowledge 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, often previously unknown, by sifting through large amounts of data, using pattern recognition, statistical and mathematical techniques

10. Steps to Data Mining Data sources Integrate data sources Clean/ modify data sources Mine the data Examine/ prune results Report/ evaluate results The cycle may continue; add new data, use different algorithms

11. What’s going on in data mining? l What are the technologies for data mining? - Database management, machine learning, statistics, pattern recognition, visualization, parallel processing,... l What can data mining do for you? - Data mining outcomes: Classification, Clustering, Association, Anomaly detection, Prediction, Estimation,... l How do you carry out data mining? - Data mining techniques: Decision trees, Neural networks, Market-basket analysis, Genetic algorithms,... l What is the current status? - Many commercial products mine relational databases l What are some of the challenges? - Mining unstructured data, extracting useful patterns, web mining

12. Infrastructure Support for Data Mining l We can have the best data mining tools, but without the infrastructure we cannot carry out effective data mining l Infrastructure includes - High performance computing systems and networks - Mass storage systems - Caches for real-time applications - Software environments for processing heterogeneous data from multiple data sources - Trained personnel - Management commitment -......

13. Data Mining Needs for Counterterrorism: Non-real-time Data Mining l Gather data from multiple sources - Information on terrorist attacks: who, what, where, when, how - Personal and business data: place of birth, ethnic origin, religion, education, work history, finances, criminal record, relatives, friends and associates, travel history,... - Unstructured data: newspaper articles, video clips, speeches, emails, phone records,... l Integrate the data, build warehouses and federations l Develop profiles of terrorists, activities/threats l Mine the data to extract patterns of potential terrorists and predict future activities and targets l Find the “needle in the haystack” - suspicious needles? l Data integrity is important l Techniques have to SCALE

14. Data Mining Needs for Counterterrorism: Real-time Data Mining l Nature of data - Data arriving from sensors and other devices l Continuous data streams - Breaking news, video releases, satellite images - Some critical data may also reside in caches l Rapidly sift through the data and discard unwanted data for later use and analysis (non-real-time data mining) l Data mining techniques need to meet timing constraints l Quality of service (QoS) tradeoffs among timeliness, precision and accuracy l Presentation of results, visualization, real-time alerts and triggers

15. Data Mining Needs for Counterterrorism: Cybersecurity l Determine nature of threats and vulnerabilities - e.g., emails, trojan horses and viruses l Classify and group the threats l Profiles of potential cyberterrorist groups and their capabilities l Data mining for intrusion detection - Real-time/ near-real-time data mining - Limit the damage before it spreads l Data mining for preventing future attacks l Data mining for Digital forensics and Biometrics

16. Data Mining Needs for Counterterrorism: Protection from Bioterrorism l Determine nature of threats - Biological weapons and agents, Chemical weapons and agents l Classify and group the threats l Identify the types of substances used l Prevention and detection mechanisms - Intelligence gathering, detecting symptoms l Determine actions to be taken to avoid fatal and dangerous situations

17. Are general data/web mining techniques sufficient? l Does one size fit all? - Non real-time, real-time, cyber, bio? l What are the major differences - e.g., develop models ahead of time for real-time data mining? - What happens in a very dynamic environment? l Data mining tasks/outcomes - Classification, clustering, associations, anomaly detection, prediction - - - -? l Data mining techniques - Which techniques are good for which problems?

18. Where are we now? l We have some tools for - building data warehouses from structured data - integrating structured heterogeneous databases - mining structured data - forming some links and associations - information retrieval tools - image processing and analysis - pattern recognition - video information processing - visualizing data - managing metadata - intrusion detection and forensics

19. What are our challenges? l Do the tools scale for large heterogeneous databases and petabyte sized databases? l Integrating structured data with unstructured data l Extracting metadata from unstructured data l Indexing unstructured data for efficient access l Mining unstructured data l Extracting useful patterns from knowledge-directed data mining l Rapidly forming links and associations; get the big picture for real- time data mining l Mining the web l Evaluating data mining algorithms l Building testbeds

20. Some other data mining applications for National Security l Insider Threat analysis l Preventing/Detecting Money laundering, Drug trafficking, Tax violations l Protecting children from inappropriate content on the Internet - National Academy of Science Panel 2000-2001 Chair: Richard Thornburgh (former U.S. Attorney General) l Protecting infrastructures, national databases, -.-.-.-

21. Form a Research Agenda l Immediate action (0 - 1 year) - We’ve got to know what our current capabilities are - Do the commercial tools scale? Do they work only on special data and limited cases? Do they deliver what they promise? - Need an unbiased objective study with demonstrations l At the same time, work on the big picture - What do we want? What are our end results for the foreseeable future? What are the criteria for success? How do we evaluate the data mining algorithms? What testbeds do we build? l Near-term (1 - 3 years) - Leverage current research - Fill the gaps in a goal-directed way l Long-term research (3 - 5 years and beyond) - 5-year basic research plan for data mining for counterterrorism

22. IN SUMMARY: l Data Mining is very useful to solve Security Problems - Data mining tools could be used to examine audit data and flag abnormal behavior - Much recent work in Intrusion detection l e.g., Neural networks to detect abnormal patterns - Tools are being examined to determine abnormal patterns for national security l Classification techniques, Link analysis - Fraud detection l Credit cards, calling cards, identity theft etc. BUT CONCERNS FOR PRIVACY

23. Some Privacy concerns l Medical and Healthcare - Employers, marketers, or others knowing of private medical concerns l Security - Allowing access to individual’s travel and spending data - Allowing access to web surfing behavior l Marketing, Sales, and Finance - Allowing access to individual’s purchases

24. Data Mining as a Threat to Privacy l Data mining gives us “facts” that are not obvious to human analysts of the data l Can general trends across individuals be determined without revealing information about individuals? l Possible threats: - Combine collections of data and infer information that is private l Disease information from prescription data l Military Action from Pizza delivery to pentagon l Need to protect the associations and correlations between the data that are sensitive or private

25. Some Problems and Solutions l Problem: Privacy violations that result due to data mining - Potential solution: Privacy-preserving data mining l Problem: Privacy violations that result due to the Inference problem - Inference is the process of deducing sensitive information from the legitimate responses received to user queries - Potential solution: Privacy Constraint Processing l Problem: Privacy violations due to un-encrypted data - Potential solution: Encryption at different levels l Problem: Privacy violation due to poor system design - Potential solution: Develop methodology for designing privacy- enhanced systems

26. Some Directions: Privacy Preserving Data Mining l Prevent useful results from mining - Introduce “cover stories” to give “false” results - Only make a sample of data available so that an adversary is unable to come up with useful rules and predictive functions l Randomization - Introduce random values into the data and/or results - Challenge is to introduce random values without significantly affecting the data mining results - Give range of values for results instead of exact values l Secure Multi-party Computation - Each party knows its own inputs; encryption techniques used to compute final results - Rules, predictive functions l Approach: Only make a sample of data available - Limits ability to learn good classifier

27. Some Directions: Privacy Controllers l Privacy controller - Augment a database system with a privacy controller for constraint processing - Content-based constraint: If document contains information about X, then it is private - Association-based Constraint: Two or more documents taken together is private; individually each document is public - Release constraint: After X is released Y becomes private l Use of conceptual structures - Design applications with privacy in mind

28. Architecture for Privacy Constraint Processing User Interface Manager Constraint Manager Privacy Constraints Query Processor: Constraints during query and release operations Update Processor: Constraints during update operation Database Design Tool Constraints during database design operation Database DBMS