Data Mining for Security Applications Dr. Bhavani Thuraisingham The University of Texas at Dallas October 2005

2 11/28/ :14 Outline 0 National Security/Cyber Security : Threats and Countermeasures 0 Overview of Data Mining 0 Data Mining for National Security/Cyber Security 0 Privacy Concerns

3 11/28/ :14 Aspects of Counterterrorism 0 National Security Measures =Protection from Non-real-time Threats =Protection from Real-time Threats 0 Protection from Cyberterrorism -Cyber security 0 Protection from Bioterrorism 0 Preventing/Detecting/Containing Terrorist activities

4 11/28/ :14 Some National Security Measures 0 Border Security -Protecting National/State Borders -Protecting Information Flows across borders -Managing and Mitigating Risks 0 First/Emergency Responses -Detecting attacks (cyber or otherwise) and responding to attacks in a timely manner -Containing the damage 0 Continued Monitoring and Management -Manage attacks, lessons learned and prevent future attacks -Surveillance, vigilance

5 11/28/ :14 Protection Objects for National Security 0 Services -Transportation, Financial, Medical, Insurance, Education, Infrastructures -Telecommunication networks, Power systems, water supply/tanks/reservoirs 0 Information related -Computing systems and networks, National databases, Financial databases., Medical databases, - - -

6 11/28/ :14 Cyber Security Measures 0 Protection from Trojan Horses and Viruses 0 Protection from Jamming 0 Recovering from network and system failures through malicious attacks 0 Intrusion detection/prevention, auditing 0 Secure clients, secure servers, secure networks and protocols, secure middleware 0 Develop and enforce security policies for Intranets and Internet 0 Secure collaboration, e-commerce, transactions 0 Access control, identification, authentication, nonrepudiation

7 11/28/ :14 National Security vs Cyber Security 0 Cannot separate the two; Much of the data is now or will be on the web 0 Digital libraries have emerged; semantic web is a matter of time 0 Example: Border security measures include physically protecting the borders as well as protecting information flow across borders 0 Transportation security 0 Effective cyber security measures could prevent national security disasters -e.g., monitoring and data on the web

8 11/28/ :14 Some common threads 0 Identify threats and group/classify threats 0 Learn from experiences, prior situations 0 Develop techniques to prevent attacks 0 Develop techniques to detect attacks, deal with attacks in a timely manner 0 Develop techniques to monitor and prevent future attacks

9 11/28/ :14 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 11/28/ :14 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 11/28/ :14 What’s going on in data mining? 0 What are the technologies for data mining? -Database management, machine learning, statistics, pattern recognition, visualization, parallel processing,... 0 What can data mining do for you? -Data mining outcomes: Classification, Clustering, Association, Anomaly detection, Prediction, Estimation,... 0 How do you carry out data mining? -Data mining techniques: Decision trees, Neural networks, Market-basket analysis, Genetic algorithms,... 0 What is the current status? -Many commercial products mine relational databases 0 What are some of the challenges? -Mining unstructured data, extracting useful patterns, web mining

12 11/28/ :14 Infrastructure Support for Data Mining 0 We can have the best data mining tools, but without the infrastructure we cannot carry out effective data mining 0 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 11/28/ :14 Data Mining Needs for Counterterrorism: Non-real-time Data Mining 0 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, s, phone records,... 0 Integrate the data, build warehouses and federations 0 Develop profiles of terrorists, activities/threats 0 Mine the data to extract patterns of potential terrorists and predict future activities and targets 0 Find the “needle in the haystack” - suspicious needles? 0 Data integrity is important 0 Techniques have to SCALE

14 11/28/ :14 Data Mining Needs for Counterterrorism: Real-time Data Mining 0 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 0 Rapidly sift through the data and discard unwanted data for later use and analysis (non-real-time data mining) 0 Data mining techniques need to meet timing constraints 0 Quality of service (QoS) tradeoffs among timeliness, precision and accuracy 0 Presentation of results, visualization, real-time alerts and triggers

15 11/28/ :14 Data Mining Needs for Counterterrorism: Cybersecurity 0 Determine nature of threats and vulnerabilities -e.g., s, trojan horses and viruses 0 Classify and group the threats 0 Profiles of potential cyberterrorist groups and their capabilities 0 Data mining for intrusion detection -Real-time/ near-real-time data mining -Limit the damage before it spreads 0 Data mining for preventing future attacks 0 Data mining for Digital forensics and Biometrics

16 11/28/ :14 Data Mining Needs for Counterterrorism: Protection from Bioterrorism 0 Determine nature of threats -Biological weapons and agents, Chemical weapons and agents 0 Classify and group the threats 0 Identify the types of substances used 0 Prevention and detection mechanisms -Intelligence gathering, detecting symptoms 0 Determine actions to be taken to avoid fatal and dangerous situations

17 11/28/ :14 Are general data/web mining techniques sufficient? 0 Does one size fit all? -Non real-time, real-time, cyber, bio? 0 What are the major differences -e.g., develop models ahead of time for real-time data mining? -What happens in a very dynamic environment? 0 Data mining tasks/outcomes -Classification, clustering, associations, anomaly detection, prediction ? 0 Data mining techniques -Which techniques are good for which problems?

18 11/28/ :14 Where are we now? 0 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 11/28/ :14 What are our challenges? 0 Do the tools scale for large heterogeneous databases and petabyte sized databases? 0 Integrating structured data with unstructured data 0 Extracting metadata from unstructured data 0 Indexing unstructured data for efficient access 0 Mining unstructured data 0 Extracting useful patterns from knowledge-directed data mining 0 Rapidly forming links and associations; get the big picture for real- time data mining 0 Mining the web 0 Evaluating data mining algorithms 0 Building testbeds

20 11/28/ :14 Mining Multimedia Data 0 Mining text for extracting associations between documents and words -Critical for intelligence analysis 0 Mining images to detect anomalies -e.g., some unidentified object appears within a certain time 0 Mining video -For unusual patterns, appearance of certain objects in the video

21 11/28/ :14 Tools for the Analyst 0 Develop tools for the analyst for non real-time and real-time data mining -Analyst is flooded with reports and articles -Need tools for =Managing the data, organizing the data, mining the data, text summarization, enforcing triggers and alerts 0 Example: Mine the documents to extract relevant documents, translate the relevant documents if needed, summarize the documents -Multi-lingual data mining 0 Conduct a pilot study to determine whether the events of September 11th could have been prevented with data mining tools

22 11/28/ :14 Some other data mining applications for National Security 0 Insider Threat analysis 0 Preventing/Detecting Money laundering, Drug trafficking, Tax violations 0 Protecting children from inappropriate content on the Internet -National Academy of Science Panel Chair: Richard Thornburgh (former U.S. Attorney General) 0 Protecting infrastructures, national databases,

23 11/28/ :14 Form a Research Agenda 0 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 0 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? 0 Near-term (1 - 3 years) -Leverage current research -Fill the gaps in a goal-directed way 0 Long-term research (3 - 5 years and beyond) -5-year basic research plan for data mining for counterterrorism

24 11/28/ :14 IN SUMMARY: 0 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 =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

25 11/28/ :14 Some Privacy concerns 0 Medical and Healthcare -Employers, marketers, or others knowing of private medical concerns 0 Security -Allowing access to individual’s travel and spending data -Allowing access to web surfing behavior 0 Marketing, Sales, and Finance -Allowing access to individual’s purchases

26 11/28/ :14 Data Mining as a Threat to Privacy 0 Data mining gives us “facts” that are not obvious to human analysts of the data 0 Can general trends across individuals be determined without revealing information about individuals? 0 Possible threats: -Combine collections of data and infer information that is private =Disease information from prescription data =Military Action from Pizza delivery to pentagon 0 Need to protect the associations and correlations between the data that are sensitive or private

27 11/28/ :14 Some Privacy Problems and Potential Solutions 0 Problem: Privacy violations that result due to data mining -Potential solution: Privacy-preserving data mining 0 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 0 Problem: Privacy violations due to un-encrypted data -Potential solution: Encryption at different levels 0 Problem: Privacy violation due to poor system design -Potential solution: Develop methodology for designing privacy- enhanced systems

28 11/28/ :14 Some Directions: Privacy Preserving Data Mining 0 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 0 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 0 Secure Multi-party Computation -Each party knows its own inputs; encryption techniques used to compute final results -Rules, predictive functions 0 Approach: Only make a sample of data available -Limits ability to learn good classifier

29 11/28/ :14 Some Directions: Privacy Problem as a form of Inference Problem 0 Privacy constraints -Content-based constraints; association-based constraints 0 Privacy controller -Augment a database system with a privacy controller for constraint processing and examine the releasability of data/information (e.g., release constraints) 0 Use of conceptual structures to design applications with privacy in mind (e.g., privacy preserving database and application design) 0 The web makes the problem much more challenging than the inference problem we examined in the 1990s! 0 Is the General Privacy Problem Unsolvable?

30 11/28/ :14 Privacy Constraint Processing 0 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 0 Augment a database system with a privacy controller for constraint processing

31 11/28/ :14 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

32 11/28/ :14 Semantic Model for Privacy Control Patient John Cancer Influenza Has disease Travels frequently England address John’s address Dark lines/boxes contain private information

33 11/28/ :14 Some Directions: Encryption for Privacy 0 Encryption at various levels -Encrypting the data as well as the results of data mining -Encryption for multi-party computation 0 Encryption for untrusted third party publishing -Owner enforces privacy policies -Publisher gives the user only those portions of the document he/she is authorized to access -Combination of digital signatures and Merkle hash to ensure privacy

34 11/28/ :14 Some Directions: Methodology for Designing Privacy Systems 0 Jointly develop privacy policies with policy specialists 0 Specification language for privacy policies 0 Generate privacy constraints from the policy and check for consistency of constraints 0 Develop a privacy model 0 Privacy architecture that identifies privacy critical components 0 Design and develop privacy enforcement algorithms 0 Verification and validation

35 11/28/ :14 Data Mining and Privacy: Friends or Foes? 0 They are neither friends nor foes 0 Need advances in both data mining and privacy 0 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 0 Technologists, legal specialists, social scientists, policy makers and privacy advocates MUST work together

36 11/28/ :14 Ideas and Directions? Prof. Bhavani Thuraisingham -Director Cyber Security Center -Department of Computer Science -Erik Jonsson School of Engineering and Computer Science -The University of Texas at Dallas -Richardson, Texas President Dr-Bhavani Security Consulting Dallas, TX