Data and Applications Security Developments and Directions

1. Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #17 Data Mining, Security and Privacy March 15, 2006

2. Objective of the Unit • This unit provides an overview of data mining for security (national security and information security) and then discuss privacy

3. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Why We Need Intrusion Detection Systems? Incidents Reported to Computer Emergency Response Team/Coordination Center (CERT/CC) • Due to the proliferation of high-speed Internet access, more and more organizations are becoming vulnerable to potential cyber attacks, such as network intrusions • Sophistication of cyber attacks as well as their severity has also increased recently (e.g., Code-Red I & II, Nimda, and more recently the SQL slammer worm on Jan. 25) • Security mechanisms always have inevitable vulnerabilities • Current firewalls are not sufficient to ensuresecurity in computer networks Source: www.caida.org The geographic spread of Sapphire/Slammer Worm 30 minutes after release

4. Data Mining for Intrusion Detection • Increased interest in data mining based intrusion detection • Attacks for which it is difficult to build signatures; Unforeseen/Unknown/Emerging attacks; Distributed/coordinated attacks • Data mining approaches for intrusion detection • Misuse detection • Building predictive models from labeled labeled data sets (instances are labeled as “normal” or “intrusive”) to identify known intrusions • High accuracy in detecting many kinds of known attacks • Cannot detect unknown and emerging attacks • Anomaly detection • Detect novel attacks as deviations from “normal” behavior • Potential high false alarm rate - previously unseen (yet legitimate) system behaviors may also be recognized as anomalies

5. Outline: Data Mining for Security (National and Cyber) • Data Mining for Intrusion Detection • General discussions on data mining for counter-terrorism • Data mining for non real-time threats and real-time threats • Data mining for cyber terrorism and bioterrorism • Discussions of some techniques • Directions and challenges

6. Data Mining for Counter-terrorism

7. Data Mining Needs for Counterterrorism: Non-real-time Data Mining • 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, . . . • Integrate the data, build warehouses and federations • Develop profiles of terrorists, activities/threats • Mine the data to extract patterns of potential terrorists and predict future activities and targets • Find the “needle in the haystack” - suspicious needles? • Data integrity is important • Techniques have to SCALE

8. Data Mining for Non Real-time Threats Clean/ Integrate Build modify data Profiles data of Terrorists sources and Activities sources Mine Data sources the with information about terrorists data and terrorist activities Report Examine final results/ results Prune results

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

10. Data Mining for Real-time Threats Rapidly Integrate Build sift through data and data real - time discard models sources in irrelevant real - time data Mine Data sources the with information about terrorists data and terrorist activities Report Examine final Results in results Real - time

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

12. Data Mining Needs for Counterterrorism: Protection from Bioterrorism • Determine nature of threats • Biological weapons and agents, Chemical weapons and agents • Classify and group the threats • Identify the types of substances used • Prevention and detection mechanisms • Intelligence gathering, detecting symptoms, biosensors • Determine actions to be taken to avoid fatal and dangerous situations • Need data management engineers, data miners, computational scientists, mathematical biologists, epidemiologists to work together • Model the spread of diseases, detection and prevention

13. Some common threads • Identify the threats • Group/classify the threats • Gather data; Develop profiles of terrorists • Data mining for preventing/detecting/managing terrorist attacks

14. Data Mining Outcomes and Techniques for Counter-terrorism

15. Web Usage Mining for Counter-terrorism

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

17. Some other data mining applications for National Security • Insider Threat analysis • Detecting potential threats from employees of a corporation or agencies • E.g., Espionage • Preventing/Detecting Money laundering, Drug trafficking, Tax violations • Protecting children from inappropriate content on the Internet • National Academy of Science Panel 2000-2001 Chair: Richard Thornburgh (former U.S. Attorney General) • Protecting infrastructures, national databases, -.-.-.-

18. Example Success Story - COPLINK • COPLINK developed at University of Arizona • Research transferred to an operational system currently in use by Law Enforcement Agencies • What does COPLINK do? • Provides integrated system for law enforcement; integrating law enforcement databases • If a crime occurs in one state, this information is linked to similar cases in other states • It has been stated that the sniper shooting case may have been solved earlier if COPLINK had been operational at that time

19. Where are we now? • 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

20. What are our challenges? • Do the tools scale for large heterogeneous databases and petabyte sized databases? • Building models in real-time; need training data • Extracting metadata from unstructured data • Mining unstructured data • Extracting useful patterns from knowledge-directed data mining • Rapidly forming links and associations; get the big picture for real-time data mining • Detecting/preventing cyber attacks • Mining the web • Evaluating data mining algorithms • Conducting risks analysis / economic impact • Building testbeds

21. Form a Work Agenda • 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 • 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? • Near-term (1 - 3 years) • Leverage current efforts • Fill the gaps in a goal-directed way; technology transfer • Long-term (3 - 5 years and beyond) • 5-year R&D plan for data mining for counterterrorism

22. IN SUMMARY: • 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 (unit #18) • 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. BUT CONCERNS FOR PRIVACY

23. Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Privacy March 29, 2005

24. Outline • Data Mining and Privacy - Review • Some Aspects of Privacy • Revisiting Privacy Preserving Data Mining • Platform for Privacy Preferences • Challenges and Discussion

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

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

27. Some Privacy Problems and Potential Solutions • Problem: Privacy violations that result due to data mining • Potential solution: Privacy-preserving data mining • 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 • Problem: Privacy violations due to un-encrypted data • Potential solution: Encryption at different levels • Problem: Privacy violation due to poor system design • Potential solution: Develop methodology for designing privacy-enhanced systems

28. Some Directions:Privacy Preserving Data Mining • 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 • 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 • Secure Multi-party Computation • Each party knows its own inputs; encryption techniques used to compute final results • Rules, predictive functions • Approach: Only make a sample of data available • Limits ability to learn good classifier

29. Privacy Preserving Data MiningAgrawal and Srikant (IBM) • Value Distortion • Introduce a value Xi + r instead of Xi where r is a random value drawn from some distribution • Uniform, Gaussian • Quantifying privacy • Introduce a measure based on how closely the original values of modified attribute can be estimated • Challenge is to develop appropriate models • Develop training set based on perturbed data • Evolved from inference problem in statistical databases

30. Privacy Constraint Processing • Privacy constraints processing • Based on prior research in security constraint processing • Simple Constraint: an attribute of a document is private • 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 • Augment a database system with a privacy controller for constraint processing

31. Architecture for 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

32. Semantic Model for Privacy Control Dark lines/boxes contain private information Cancer Influenza Has disease John’s address Patient John England address Travels frequently

33. Data Mining and Privacy: Friends or Foes? • They are neither friends nor foes • Need advances in both data mining and privacy • Need to design flexible systems • For some applications one may have to focus entirely on “pure” data mining while for some others there may be a need for “privacy-preserving” data mining • Need flexible data mining techniques that can adapt to the changing environments • Technologists, legal specialists, social scientists, policy makers and privacy advocates MUST work together

34. Platform for Privacy Preferences (P3P): What is it? • P3P is an emerging industry standard that enables web sites t9o express their privacy practices in a standard format • The format of the policies can be automatically retrieved and understood by user agents • It is a product of W3C; World wide web consortium www.w3c.org • Main difference between privacy and security • User is informed of the privacy policies • User is not informed of the security policies

35. Platform for Privacy Preferences (P3P): Key Points • When a user enters a web site, the privacy policies of the web site is conveyed to the user • If the privacy policies are different from user preferences, the user is notified • User can then decide how to proceed

36. Platform for Privacy Preferences (P3P): Organizations • Several major corporations are working on P3P standards including: • Microsoft • IBM • HP • NEC • Nokia • NCR • Web sites have also implemented P3P • Semantic web group has adopted P3P

37. Platform for Privacy Preferences (P3P): Specifications • Initial version of P3P used RDF to specify policies • Recent version has migrated to XML • P3P Policies use XML with namespaces for encoding policies • Example: Catalog shopping • Your name will not be given to a third party but your purchases will be given to a third party • <POLICIES xmlns = http://www.w3.org/2002/01/P3Pv1> <POLICY name = - - - - </POLICY> </POLICIES>

38. Platform for Privacy Preferences (P3P): Specifications (Concluded) • P3P has its own statements a d data types expressed in XML • P3P schemas utilize XML schemas • XML is a prerequisite to understanding P3P • P3P specification released in January 20005 uses catalog shopping example to explain concepts • P3P is an International standard and is an ongoing project

39. P3P and Legal Issues • P3P does not replace laws • P3P work together with the law • What happens if the web sites do no honor their P3P policies • Then appropriate legal actions will have to be taken • XML is the technology to specify P3P policies • Policy experts will have to specify the policies • Technologies will have to develop the specifications • Legal experts will have to take actions if the policies are violated

40. Challenges and Discussion • Technology alone is not sufficient for privacy • We need technologists, Policy expert, Legal experts and Social scientists to work on Privacy • Some well known people have said ‘Forget about privacy” • Should we pursue working on Privacy? • Interesting research problems • Interdisciplinary research • Something is better than nothing • Try to prevent privacy violations • If violations occur then prosecute • Discussion?