Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis Presented by Yang Gao 11/2/2011 Charles V. Wright MIT Lincoln Laboratory Scott E. Coull Johns Hopkins University Fabian Monrose University of North Carolina

Outline  Potential Hazards  Counter measures and Traffic Morphing  How it works?  Evaluation and Results

Privacy Security

Packet Size and Timing Information Privacy Leakage Classification Tools Language of a VoIP call Password in SSH Web browsing habits...

How does the attack happen  Webpage browsing  Statistical Identification of Encrypted Web Browsing Traffic (Sun,Q. Stanford University)

A 2000 sample from 100,000 WebPages Only Objects number and sizes are recorded Jaccard’s coefficient Trained classifier

How does the attack happen  Webpage browsing  Statistical Identification of Encrypted Web Browsing Traffic (Sun,Q. Et Stanford University)  Inferring the Source of Encrypted HTTP Connections (Marc Liberatore and Brian Neil Levine UMA)  Identification of Encrypted VoIP Traffic

Results of the Classifiers

Outline  Potential Hazards  Counter measures and Traffic Morphing  How it works?  Evaluation and Results

Countermeasures  Padding  Mimicking  Morphing  Sending at fixed time intervals(counter the timing analysis)

Comparison

Traffic Morphing morphing

How does the morphing work? L1L2L1 L2L1L2 N L1 : N L2 = 2 : 1 N L1 : N L2 = 1 : 2

Outline  Potential Hazards  Counter measures and Traffic Morphing  How it works?  Evaluation and Results

Traffic Morphing  Goals  Good resemblance in packet size distribution  Less overhead  Steps  Morphing matrix construction

Morphing Matrix Size x1 Size xn Size y1 Size yn 2*n equations and n 2 unknowns

How to solve these equations?  We won't solve them directly.  Convex Optimization  Cost Function  Restrictions

Example L1L2L1 L2L1L2

Example L1L2L1 L2 Reduce? Add more constrains to avoid this situation.

Steps for Traffic Morphing  Matrix Construction  Select the source process and calculate the probability distribution of the packets size.  Select the target process and calculate the probability distribution of the packets size.  Solve the morphing matrix with optimization method which could minimize the cost while following the restrictions.  Traffic Morphing  Get the packet to send.  set up a random number to select the element in the matrix  Calculate the corresponding packet size.  Padding or reduce the packet size  Transmit the new packet.

Traffic Morphing  Goals  Good resemblance in packet size distribution  Less overhead  Steps  Morphing matrix construction  Additional Morphing Constraints

Pitfall 1  System is over-specified  Y = AX  Solution:  Multi-level programming  Find Z which is closest to Y  Find A which such that most efficiently maps X to Z  Z=A’X => Minimize( f d (Y,Z) )  Z=AX => Minimize( f 0 (A) )

Traffic Morphing  Goals  Good resemblance in packet size distribution  Less overhead  Steps  Morphing matrix construction  Additional Morphing Constraints  Dealing with Large Sample Spaces

Pitfall 2  Pool Scalability  Pentium 4 2.8G run 1 hr for 80x80 matrix with 6560 constraints  MTU(40~1500) means 1460x1460 Matrix  Solution  Multi-level method  Sub-matrix Morphing

Multi-level method

Traffic Morphing in sum  Goals  Good resemblance in packet size distribution  Less overhead  Steps  Morphing matrix construction  Convex optimization  Additional Morphing Constraints  2 level Multi-level programming  Dealing with Large Sample Spaces  Sub-matrix Morphing

Outline  Potential Hazards  Counter measures and Traffic Morphing  How it works?  Evaluation and Results

Evaluation  Encrypted Voice over IP  Web Page Identification  Defeating Original Classifier  Evaluating Indistinguishability

Encrypted Voice over IP  Language Identification of Encrypted VoIP Traffic:Alejandra y Roberto or Alice and Bob?  Charles V. Wright Lucas Ballard Fabian Monrose Gerald M. Masson from Department of Computer Science Johns Hopkins University

White box encode

Why even the encrypted voice packet will leak information  Unigram frequencies of bit rates

2-gram resemblance

Blackbox

Results for original classifier

Results for Indistinguishablity

Overhead

Web page Identification

Overhead

Practical Considerations  Short Network Sessions  Short of packets generated by source?  Keep generating until reach a distance threshold  Variations in Source Distribution  Packets size difference for training and using?  Divide and conquer  Reduced Packet Sizes  How to deal with the reduced packet size in HTTP  Packing to the next

Traffic Morphing in a nut shell  Resemblance  Morphing Matrix  Convex Optimization  Overhead Minimization  Additional Morphing Constraints  Dealing with Large Sample Spaces  Practical Considerations  Short Network Sessions  Variations in Source Distribution  Reduced Packet Sizes

Conclusion  User privacy are vulnerable even under encryption protected.  Traffic morphing is effective and robust  Traffic morphing is applicable.  Traffic morphing is much more efficient than padding.

Discussion  The other side of morphing  Anti-intrude-detection.  Mimicry attack System call sequence Malicious call combination library deny accept morphing

Thank you!