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Faster Differentiation of Terrorists and Malicious Cyber Transactions from Good People and Transactions. Peter P. Chen Foster Distinguished Chair Professor Computer Science Dept. Louisiana State University Baton Rouge, LA 70803, USA [email protected] http://www.csc.lsu.edu/~chen.

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Faster Differentiation of Terrorists and Malicious Cyber Transactions from Good People and Transactions

Peter P. Chen

Foster Distinguished Chair Professor

Computer Science Dept.

Louisiana State University

Baton Rouge, LA 70803, USA

[email protected]

http://www.csc.lsu.edu/~chen


Profiling of terrorists and malicious cyber transactions l.jpg
Profiling of terrorists and malicious cyber transactions Transactions from Good People and Transactions

  • Examples: 9-11, Airport Security, D.C. snipers, Louisiana serial killer, Ohio sniper, etc.

  • Current Problems:

    • Isolated Data

    • Questionable data

    • Little Mathematical Analysis

    • Algorithms (if any) are independent of (or incompatible with) data models


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Why Do We Study the Profiling Problem? Transactions from Good People and Transactions

  • 9-11

  • D.C. snipers

  • serial killers in Louisiana, California, etc.

  • Ohio sniper, etc.

  • Airport Security


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In any population, … Transactions from Good People and Transactions


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Attributes (and “relationships) of bad guys Transactions from Good People and Transactions

  • Black hair?

  • Beard/moustache?

  • Nationality: xxxx?

  • Has traveled to Country X three times?


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Using the fewest attributes to catch all the bad guys … Transactions from Good People and Transactions

  • black hair

  • beard/moustache


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…also catches some good guys (casualties): Transactions from Good People and Transactions

  • black hair

  • beard/moustache


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…also catches some good guys (casualties): Transactions from Good People and Transactions

  • black hair

  • beard/moustache


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Goal Transactions from Good People and Transactions: • Find the smallest number of attributes that will catch all the bad guys, but at the same time• Include as few casualties (good guys) as possible.


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Some good guys are more important than others Transactions from Good People and Transactions


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Some bad guys are more important (to capture) than others Transactions from Good People and Transactions


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Goal (more ambitious): Transactions from Good People and Transactions• Find the smallest number of attributes that will catch as many, and preferably the more important bad guys,but at the same time• Include as few, and preferably the less important good guys, as possible.


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Problem -- Profiling of Terrorists and malicious cyber transactions

  • Current Problems:

    • Isolated Data

    • Questionable data

    • Little Mathematical Analysis

    • “Unscientific/Unproven” Methods

    • Algorithms (if any) are independent of (or incompatible with) data models

  • Solution:

    • Data “links” (“relationships”)

    • Info validity and conflict resolution

    • Optimization model & algorithms

    • Integration of data model and algorithms


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Solution Techniques for the Profiling Problem (I) – „New“ Concepts of ERM

  • Discovering „Links/Relationships“ from Data in Various Sources (such as DARPA‘s EELD Program)

  • „Auto“-construction of „Relationships“

  • „Dynamically adjusting“ the weights of relationships

  • Validity/Credibility Analysis of Data

    • A Paper was published in InfoFusion 2001, Montreal

    • Algorithm was developed

    • Prototype developed

    • Also, developed machine learning algorithm


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Solution Techniques for the Profiling problem (II) – (a) Integration of ERM and Math Models, (b) Developing New Math Models & Algorithms

  • We Model the „profiling“ problem as a „generalized set covering problem“

    • Start with the conventional definition of a „set covering problem (SCP)“

    • Then, define a „weighted set covering problem“

    • Finally, define a „generalized set covering problem“

  • We have developed several efficient algorithms for solving this type of problems. Some of them are modified versions of the „greedy algorithm“

  • Based on our tests, these new algorithms perform better than other algorithms in the SCP case

  • We have also obtained and proved some computational complexity bounds


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The Set Covering Problem ( Integration of ERM and Math Models, SCP)


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Notation Integration of ERM and Math Models,


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SET COVERING PROBLEM ( Integration of ERM and Math Models, SCP) definition:


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Notation 2 Integration of ERM and Math Models,


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WEIGHTED SET COVERING PROBLEM Integration of ERM and Math Models, (WSCP) definition:


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GSCP generalizes WSCP in three aspects: Integration of ERM and Math Models,

  • Each SiS is associated with a weighted set WiW, where W = {W1, W2, … , Wn} and WiG, 1 ≤ i ≤ n, where G is a finite set.

  • Each element bB is weighted.

  • A combination of weighted elements of B with an additional factor  enables a relaxation of the covering requirement.


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GENERALIZED SET COVERING PROBLEM Integration of ERM and Math Models, (GSCP) definition:


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Algorithms for GSCP Integration of ERM and Math Models,


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Greedy Set Covering Algorithm (GSCA) Integration of ERM and Math Models,


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. Integration of ERM and Math Models,


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Generous Set Covering Algorithm (GSCGA) Integration of ERM and Math Models,


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Algorithm Integration of ERM and Math Models, Liability_1

Input: S, A, W, jN+

Output: cost

1. costc (Wj)

Algorithm Liability_2

Input: S, A, W, jN+

Output: cost

1. costc (Wj) / d (Sj)


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Super Greedy (Generous) Algorithm Integration of ERM and Math Models,


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Democratic Algorithm Integration of ERM and Math Models,


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Comparisons of Different Algorithms Integration of ERM and Math Models,


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Table notation Integration of ERM and Math Models,



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Table 2. Outputs to instances of algorithmsSCP by various heuristic algorithms


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Table 3. Number of basic operations executed by the Democratic Algorithm using various configurations to solve instances of SCP


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Table 5. Output of the Democratic Algorithm using Balas/Carrera and Beasley’s algorithms


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Which Algorithm is the best? Balas/Carrera and Beasley’s algorithms


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Near-Term Research Plans -- Balas/Carrera and Beasley’s algorithms

  • Take advantage of LSU’s NCSRT, one of the largest training centers of emergency and anti-terrorism workers

  • Test the Models and algorithms with law enforcement agencies and other agencies

  • Test the data-model/math-model integration problems with real and quasi-real data sets


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Other Related Research Activities Balas/Carrera and Beasley’s algorithms

  • Integration of conceptual models (ER model, etc.) with databases, math models

  • New Machine Learning Techniques

  • Trustworthiness of Data and Conflict Resolutions

  • (High and low-level) System Architecture and Cyber Security

  • Cost/Effective Assessments of Security Techniques -- Making real impacts!


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