Data and applications security developments and directions
1 / 40

Data and Applications Security Developments and Directions - PowerPoint PPT Presentation

  • Uploaded on

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. Objective of the Unit.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Data and Applications Security Developments and Directions' - wayne-mcdowell

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Data and applications security developments and directions

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

Objective of the unit
Objective of the Unit

  • This unit provides an overview of data mining for security (national security and information security) and then discuss privacy

Why we need intrusion detection systems

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


The geographic spread of Sapphire/Slammer Worm 30 minutes after release

Data mining for intrusion detection
Data Mining for Intrusion Detection 1999 2000 2001 2002

  • 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

Outline data mining for security national and cyber
Outline: Data Mining for Security (National and Cyber) 1999 2000 2001 2002

  • 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

Data mining needs for counterterrorism non real time data mining
Data Mining Needs for Counterterrorism: 1999 2000 2001 2002Non-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

Data mining for non real time threats
Data Mining for Non Real-time Threats 1999 2000 2001 2002








of Terrorists


and Activities



Data sources


with information

about terrorists


and terrorist activities








Data mining needs for counterterrorism real time data mining
Data Mining Needs for Counterterrorism: 1999 2000 2001 2002Real-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

Data mining for real time threats
Data Mining for Real-time Threats 1999 2000 2001 2002




sift through

data and







sources in







Data sources


with information

about terrorists


and terrorist activities




Results in





Data mining needs for counterterrorism cybersecurity
Data Mining Needs for Counterterrorism: 1999 2000 2001 2002Cybersecurity

  • 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

Data mining needs for counterterrorism protection from bioterrorism
Data Mining Needs for Counterterrorism: 1999 2000 2001 2002Protection 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

Some common threads
Some common threads 1999 2000 2001 2002

  • Identify the threats

  • Group/classify the threats

  • Gather data; Develop profiles of terrorists

  • Data mining for preventing/detecting/managing terrorist attacks

Are general data web mining techniques sufficient
Are general data/web mining techniques sufficient? 1999 2000 2001 2002

  • 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?

Some other data mining applications for national security
Some other data mining applications for 1999 2000 2001 2002National 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, -.-.-.-

Example success story coplink
Example Success Story - COPLINK 1999 2000 2001 2002

  • 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

Where are we now
Where are we now? 1999 2000 2001 2002

  • 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

What are our challenges
What are our challenges? 1999 2000 2001 2002

  • 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

Form a work agenda
Form a Work Agenda 1999 2000 2001 2002

  • 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

In summary
IN SUMMARY: 1999 2000 2001 2002

  • 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.


Data and applications security developments and directions1

Data and Applications Security 1999 2000 2001 2002Developments and Directions

Dr. Bhavani Thuraisingham

The University of Texas at Dallas


March 29, 2005

Outline 1999 2000 2001 2002

  • Data Mining and Privacy - Review

  • Some Aspects of Privacy

  • Revisiting Privacy Preserving Data Mining

  • Platform for Privacy Preferences

  • Challenges and Discussion

Some privacy concerns
Some Privacy concerns 1999 2000 2001 2002

  • 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

Data mining as a threat to privacy
Data Mining as a Threat to Privacy 1999 2000 2001 2002

  • 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

Some privacy problems and potential solutions
Some Privacy Problems and Potential Solutions 1999 2000 2001 2002

  • 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

Some directions privacy preserving data mining
Some Directions: 1999 2000 2001 2002Privacy 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

Privacy preserving data mining agrawal and srikant ibm
Privacy Preserving Data Mining 1999 2000 2001 2002Agrawal 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

Privacy constraint processing
Privacy Constraint Processing 1999 2000 2001 2002

  • 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

Architecture for privacy constraint processing
Architecture for Privacy 1999 2000 2001 2002Constraint Processing

User Interface Manager

Privacy Constraints



Database Design Tool

Constraints during database design operation

Update Processor:

Constraints during update operation

Query Processor:

Constraints during query and release operations



Semantic model for privacy control
Semantic Model for Privacy Control 1999 2000 2001 2002

Dark lines/boxes contain

private information



Has disease



Patient John



Travels frequently

Data mining and privacy friends or foes
Data Mining and Privacy: Friends or Foes? 1999 2000 2001 2002

  • 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

Platform for privacy preferences p3p what is it
Platform for Privacy Preferences (P3P): 1999 2000 2001 2002What 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

  • Main difference between privacy and security

    • User is informed of the privacy policies

    • User is not informed of the security policies

Platform for privacy preferences p3p key points
Platform for Privacy Preferences (P3P): 1999 2000 2001 2002Key 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

Platform for privacy preferences p3p organizations
Platform for Privacy Preferences (P3P): 1999 2000 2001 2002Organizations

  • 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

Platform for privacy preferences p3p specifications
Platform for Privacy Preferences (P3P): 1999 2000 2001 2002Specifications

  • 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 =>

      <POLICY name = - - - -



Platform for privacy preferences p3p specifications concluded
Platform for Privacy Preferences (P3P): 1999 2000 2001 2002Specifications (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

P3p and legal issues
P3P and Legal Issues 1999 2000 2001 2002

  • 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

Challenges and discussion
Challenges and Discussion 1999 2000 2001 2002

  • 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?