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Automated Reassembly of Document Fragments

Automated Reassembly of Document Fragments. DFRWS 2002. Outline. Introduction & Motivation Stages in Reassembly Reassembly Problem Our Solution Implementation & Experiments Summary. Introduction & Motivation. Introduction. Reassembly of objects from mixed fragments Common problem in:

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Automated Reassembly of Document Fragments

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  1. Automated Reassembly of Document Fragments DFRWS 2002

  2. Outline • Introduction & Motivation • Stages in Reassembly • Reassembly Problem • Our Solution • Implementation & Experiments • Summary

  3. Introduction & Motivation

  4. Introduction • Reassembly of objects from mixed fragments • Common problem in: • Classical Forensics • Failure Analysis • Archaeology • Well studied, automated… • Is there a similar problem in digital forensics?

  5. F1 Split F2 Evidence F3 Fn-1 Keywords Fn Fragments Motivation • Digital evidence is • malleable • easily scattered • Fragmentation process

  6. Motivation… • Scenarios… • Hiding in Slack Space • Criminal splits the document and hides them selectively into slack spaces based on a password • Swap File • Addressing & state information is not available on the disk • Peer-to-peer systems • Fragments are assigned a sequence of keywords and scattered across the network • e.g.: FreeNet, M-o-o-t

  7. Stages of Reassembly

  8. F1 F2 Fx preprocessing collating reassembly G1 G1 Gy H1 H1 Hz Stages of Reassembly F1 F G1 G Fx H1 H Gy Hz

  9. Stages of Reassembly • Preprocessing • Cryptanalysis • Weight Assignments • Collating • Group together fragments of a document • Hierarchical approach • Reassembly • Reordering the fragments to form the original document

  10. Reassembly

  11. The Problem of Reassembly • Suppose we have fragments {A0, A1, … An} of document A • Compute a permutation X such that A= AX(0)||AX(1)|| … AX(n) • To compute A, we need to find adjacent fragments • To reassemble: • Need to find adjacent fragments • Automate the process

  12. wn fox jumps the quick bro over the lazy dog Quantifying Adjacency • An Example: A linguist may assign probabilities based on syntactic and semantic analysis • This process is language dependent

  13. abracadabra abr a context prediction Context-Based Statistical Models • Context based models are used in data compression • Predicts subsequent symbols based on current context • Works well on natural languages as well as other data types • Context models can be used to predict upcoming symbols and assign candidate probabilities

  14. Adjacency Matrix • Candidate probabilities of each pair of fragments form complete graph • A Hamiltonian path that maximizes the sum of candidate probabilities is our solution • But this problem is intractable • We will discuss a near optimal solution a b c d e a 0 c(a,b) …. b c(b,a)0 …. c d e c(e,a) a e b d c

  15. Steps in Reassembling • Build context model using all the fragments • Compute candidate probabilities for each pair • Find a Hamiltonian Path that maximizes the sum of candidate probabilities

  16. Implementation & Experiments

  17. Prediction by Partial Matching (PPM) abracadabra • Uses a suite of fixed order context models • Uses one or more orders to predict upcoming symbol • We process each fragment with PPM • Combine the statistics to form a model for all the fragments

  18. Candidate Probability • Slide a window of size d from one fragment into the other • At each position, use the window as context and determine the probability (pi) of next symbol • Candidate prob. C(1,2) = (p0* p1*…pd) fragment 1 fragment 2 a b r a c a d a b r a c d a b a

  19. a a d b c e c d e e b c e d e c c Solution Tree • Assumtions: • Fragments are recovered without data loss • First fragment is known/easily identified • Paths in complete path can be represented as a tree • Tree grows exponentially! • We have to prune the tree

  20. c d e c e b c e c c b Pruning a • At every level choose a node with the largest candidate probability • We can choose alpha nodes at each level • By looking at candidate probabilities beta levels deep d b c e b c e

  21. Experiments

  22. Data Set

  23. Reassembly for various types

  24. Compression ratio

  25. F1 Reassembly Concatenate Final Sequence F2 Fn Concatenated fragments Iterative Approach

  26. Summary • Introduced reassembly of scattered evidence • Experiments & results • Future work: • Identifying preprocessing heuristics • Compare performance with other models • Work on reassembling images

  27. Questions

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