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Presented by Sruthi Vemulapalli

Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis Charles Wright, Scott Coull , Fabian Monrose. Presented by Sruthi Vemulapalli. Introduction. Network traffic analysis How to reduce the leak of data? Convex optimization Examples

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Presented by Sruthi Vemulapalli

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  1. Traffic Morphing: An Efficient DefenseAgainst Statistical Traffic AnalysisCharles Wright, Scott Coull, Fabian Monrose Presented by SruthiVemulapalli

  2. Introduction • Network traffic analysis • How to reduce the leak of data? • Convex optimization • Examples • Traffic classification techniques • VoIP language classifier • Web page classifier

  3. Statistical distribution in encrypted VoIP • Mimicry attack • Polymorphic blending technique • Other approaches

  4. Traffic Morphing • Goal: To provide users with an efficient method of preventing information leakage that induces less overhead. • Operation : • Selection of source processes • Selection of target processes • Morphing Matrix • Morphing algorithm • Data interception

  5. Morphing Matrix • Source process : X = [x1, x2, . . . , xn]T, xi is the probability of the ithlargest packet size • Target process : Y = [y1, y2, . . . , yn]T • Morphing Matrix A = [aij], where Y=AX

  6. Operation • Packet received from source application • Altering of packets • Cumulative probability si=sum of the probabilities for all sizes <=si • Sampling Target size • Advantage : • Minimum overhead • Matrix generation performed offline

  7. Morphing via Convex Optimization • From A we have n2 unknowns • Y=AX representation • n equations from the matrix • Another n equations

  8. Minimizing the cost function f0(A) • Solving convex optimization functions • Example Overall cost matrix A represented as: • Optimization problem in standard form

  9. Additional Morphing Constraints • Uses: • Preserve the quality of the data • Minimize number of packets produced • Adding equality constraints • Disadvantage : Overspecified equations with no valid solution

  10. Multilevel programming • Example Comparison function: First Optimization Problem:

  11. Second Optimization Problem

  12. Dealing with Large Sample Spaces • Problem with growth of constraints Complexity of finding morphing matrices when n is large becomes prohibitively high • Divide and Conquer strategy • Applying the strategy to X and Y vectors

  13. Example (bigram distributions) Initial morphing matrix optimization: Submatrix optimization:

  14. Practical Considerations • Short Network Sessions • Variations in Source Distribution • Reducing Packet Sizes

  15. Evaluation • Encrypted Voice over IP • WhiteboxvsBlackboxMorphing

  16. Defeating the Original Classifier

  17. Evaluating Indistinguishability • White box has the best accuracy over black box

  18. Web Page Identification • Defeating the Original Classifier

  19. Conclusion • Traffic morphing, chooses the best way to alter the feature(s) of a packet • Privacy and efficiency are balanced through the use of convex optimization techniques • Works in real-time • Reduces the accuracy of the VoIP and webpage classifier

  20. QUESTIONS????

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