Data Mining for Security Applications Dr. Bhavani Thuraisingham The University of Texas at Dallas January 2006

2 10/14/ :29 Aspects of Counterterrorism 0 Types of threats -Non-real-time Threats / Real-time Threats -Cyber terrorism -Bio terrorism 0 What needs to be protected? -Services =Transportation, Financial, Medical, Infrastructures =Telecommunication networks, Power systems, water supply/tanks/reservoirs -Information related =Computing systems and networks, National databases, Financial databases., Medical databases, - -

3 10/14/ :29 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

4 10/14/ :29 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

5 10/14/ :29 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

6 10/14/ :29 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

7 10/14/ :29 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

8 10/14/ :29 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

9 10/14/ :29 Are general data 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?

10 10/14/ :29 Current Status and Challenges 0 Where are we now? -building data warehouses from structured data -integrating structured heterogeneous databases -mining structured data -forming some links and associations -image processing and analysis 0 What are our Challenges? -Scalability for petabyte sized databases? -Integrating structured data with unstructured data -Mining unstructured data -Extracting useful patterns from knowledge-directed data mining -Rapidly forming links and associations -Mining the web

11 10/14/ :29 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

12 10/14/ :29 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

13 10/14/ :29 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

14 10/14/ :29 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

15 10/14/ :29 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

16 10/14/ :29 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

17 10/14/ :29 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 Bhavani Security Consulting Dallas, TX