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Entropy/IP: Uncovering Structure in IPv6 Addresses ACM IMC 2016, Santa Monica, USA Pawe Foremski, David Plonka, Arthur Berger 1 Whats Entropy/IP? A system that automatically learns structures in Internet addresses known to be active

  1. Entropy/IP: Uncovering Structure in IPv6 Addresses ACM IMC 2016, Santa Monica, USA Paweł Foremski, David Plonka, Arthur Berger 1

  2. What’s Entropy/IP? A system that automatically learns structures in Internet addresses known to be active Combines Entropy, Machine Learning, and Probabilistic Graphical Models Goal : insight into addressing plans of IPv6 networks Application : IPv6 scanning vulnerability 2

  3. Background: IPv6 addressing Is IPv6 addressing just “more addresses”? ● Quantitative change: 2^32 --> 2^128 ● But… qualitative implications ● IPv6 made the addressing space sparse ● More freedom in address assignment 3

  4. Background: IPv6 examples How to assign an IPv6 address? (in general) ● [network ID (64 bits)] + [interface ID (64 bits)] fixed 2001:db8:0010:0001::103 structured 2001:db8:0167:1109:: 10 :901 EUI-64 2001:db8:0000:1cdf:21e:c2 ff:fe c0:11db 2001:db8:4137:9e76:3031:f3fd:bbdd:2c2a ephemeral No Single Algorithm 4

  5. Background: No Single Algorithm [network ID (64 bits)] + [interface ID (64 bits)] Interface Identifier (IID): ● Stateless Address Autoconfiguration (SLAAC) e.g. RFC 4862 ○ Static / Other ○ Network Identifier: ● Routing prefixes (e.g. BGP) ○ Static / Other ○ IPv6 networks adopt their own addressing schemes 5

  6. Background: motivations for Entropy/IP Remotely glean IPv6 addressing scheme: ● Which bits are used / unused ? ○ What are the most common values ? ○ What is the syntax ? ○ Provide supportive information for: ● Classifying addresses (e.g. host reputation) ○ Scanning / defending IPv6 scanning ○ IPv6 users: Measuring the growth of IPv6 networks ○ World >12% USA >29% Belgium >48% Why? 6

  7. Entropy/IP: operation overview 1. Entropy Analysis 2. Address Segmentation 3. Segment Mining 4. Bayesian Modeling 7

  8. 1. Entropy Analysis: input 2001:0db8:0010:0013:0000:0000:0000:07fe 2001:0db8:0010:0000:0000:0000:0000:0ed3 2001:0db8:0010:0003:0000:0000:0000:0fb5 2001:0db8:0020:d05f:882f:6082:f768:710d 2001:0db8:0010:0004:0000:0000:0000:04dc 2001:0db8:0010:0003:0000:0000:0000:03ce 2001:0db8:0010:0008:0000:0000:0000:0794 2001:0db8:0010:000a:0000:0000:0000:0923 2001:0db8:0010:0006:0000:0000:0000:003c 2001:0db8:0022:1014:aef6:60af:d029:63cd 2001:0db8:0010:0012:0000:0000:0000:0c7b 2001:0db8:0022:10c0:5100:ac7d:96f5:5851 2001:0db8:0010:0002:0000:0000:0000:0de8 2001:0db8:0010:0008:0000:0000:0000:0506 2001:0db8:0022:2053:4e6a:a11a:d57f:e26d (...) 8

  9. 1. Entropy Analysis: operation 2001:0db8:0010:001 3 :0 0 00:0000:0000:07fe For a discrete random 2001:0db8:0010:000 0 :0 0 00:0000:0000:0ed3 variable X: 2001:0db8:0010:000 3 :0 0 00:0000:0000:0fb5 2001:0db8:0020:d05 f :8 8 2f:6082:f768:710d 2001:0db8:0010:000 4 :0 0 00:0000:0000:04dc 2001:0db8:0010:000 3 :0 0 00:0000:0000:03ce 2001:0db8:0010:000 8 :0 0 00:0000:0000:0794 2001:0db8:0010:000 a :0 0 00:0000:0000:0923 2001:0db8:0010:000 6 :0 0 00:0000:0000:003c 2001:0db8:0022:101 4 :a e f6:60af:d029:63cd H( X 16 ) = 3.8 /4 2001:0db8:0010:001 2 :0 0 00:0000:0000:0c7b 2001:0db8:0022:10c 0 :5 1 00:ac7d:96f5:5851 2001:0db8:0010:000 2 :0 0 00:0000:0000:0de8 H( X 18 ) = 2.2 2001:0db8:0010:000 8 :0 0 00:0000:0000:0506 /4 2001:0db8:0022:205 3 :4 e 6a:a11a:d57f:e26d (...) 9

  10. 1. Entropy Analysis: hex character variability 10

  11. 2. Address Segmentation: group by similar entropy (T h = 0.05) 11

  12. 2. Address Segmentation: list of bit ranges Smallest RIR prefix Network ID vs. interface ID 12

  13. 3. Segment Mining: what’s inside? Extract all values D k from given segment k , and find: a) Most popular values > Q 3 + 1.5 × IQR e.g. find constants, enumerations, etc. ➢ b) Densely packed ranges of values DBSCAN(values) e.g. find adjacent subnets ➢ c) Uniform distributions DBSCAN(histogram) e.g. find counters, randoms ➢ d) Summarize what’s left [ min(D k ), max(D k ) ] 13

  14. 3. Segment Mining: output & encoding Code Value Frequency 2001:0db8:0841:2500:0000:d9a0:5345:0012 2001:0db8:08 41 :2500:0000:d9a0:5345:0012 (A1, B2, C6 , D4, E5, F1, G12, H1, I2, J3) 14

  15. 4. Bayesian Network: segment inter-dependencies 2001:0db8:0010:0004:0000:0000:0000:03cc 2001:0db8:0010:0003:0000:0000:0000:0f97 2001:0db8:0022:1028:9e83:1334:17c0:897a 2001:0db8:0022:3064:69f5:02d2:f223:8635 2001:0db8:0010:0014:0000:0000:0000:0347 2001:0db8:0010:0014:0000:0000:0000:022a 2001:0db8:0010:0005:0000:0000:0000:03ca 2001:0db8:0010:0015:0000:0000:0000:0ae9 2001:0db8:0021:0056:8032:6eb3:6098:3084 2001:0db8:0010:0003:0000:0000:0000:018b 2001:0db8:0010:0002:0000:0000:0000:0424 2001:0db8:0010:0013:0000:0000:0000:0e2f 2001:0db8:0022:20a4:3eb9:5fca:3ccb:2aae 2001:0db8:0021:0014:3326:6434:74c9:aad6 2001:0db8:0010:000f:0000:0000:0000:07bd (...) 15

  16. 4. Bayesian Network: segment inter-dependencies ( A1, B1, C1, D1, E1, F1, G3, H1, I11 ) 2001:0db8:0010:0004:0000:0000:0000:03cc ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) 2001:0db8:0010:0003:0000:0000:0000:0f97 ( A1, B1, C2, D2, E1, F5, G4, H2, I11 ) 2001:0db8:0022:1028:9e83:1334:17c0:897a ( A1, B1, C2, D3, E1, F3, G3, H2, I11 ) 2001:0db8:0022:3064:69f5:02d2:f223:8635 ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) 2001:0db8:0010:0014:0000:0000:0000:0347 ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) 2001:0db8:0010:0014:0000:0000:0000:022a ( A1, B1, C1, D1, E1, F1, G2, H1, I11 ) 2001:0db8:0010:0005:0000:0000:0000:03ca ( A1, B1, C1, D1, E1, F2, G2, H1, I11 ) 2001:0db8:0010:0015:0000:0000:0000:0ae9 ( A1, B1, C3, D1, E1, F4, G8, H2, I11 ) 2001:0db8:0021:0056:8032:6eb3:6098:3084 ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) 2001:0db8:0010:0003:0000:0000:0000:018b ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) 2001:0db8:0010:0002:0000:0000:0000:0424 ( A1, B1, C1, D1, E1, F2, G1, H1, I11 ) 2001:0db8:0010:0013:0000:0000:0000:0e2f ( A1, B1, C2, D4, E1, F6, G3, H2, I11 ) 2001:0db8:0022:20a4:3eb9:5fca:3ccb:2aae ( A1, B1, C3, D1, E1, F2, G3, H2, I11 ) 2001:0db8:0021:0014:3326:6434:74c9:aad6 ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) 2001:0db8:0010:000f:0000:0000:0000:07bd (...) 16

  17. 4. Bayesian Network: dependency graph random variable (bit segment) statistical dependencies 17

  18. 4. Bayesian Network: conditional probabilities G: F: G1 G2 G3 F1 13% 10% 10% F2 18% 20% 20% F3 13% 7% 9% F4 16% 9% 10% 18

  19. 4. Bayesian Network: how to find it? ( A1, B1, C1, D1, E1, F1, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) ( A1, B1, C2, D2, E1, F5, G4, H2, I11 ) ( A1, B1, C2, D3, E1, F3, G3, H2, I11 ) ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F1, G2, H1, I11 ) ( A1, B1, C1, D1, E1, F2, G2, H1, I11 ) ( A1, B1, C3, D1, E1, F4, G8, H2, I11 ) ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) ( A1, B1, C1, D1, E1, F2, G1, H1, I11 ) ( A1, B1, C2, D4, E1, F6, G3, H2, I11 ) ( A1, B1, C3, D1, E1, F2, G3, H2, I11 ) ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) 19

  20. 4. Bayesian Network: BNfinder ( A1, B1, C1, D1, E1, F1, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) ( A1, B1, C2, D2, E1, F5, G4, H2, I11 ) ( A1, B1, C2, D3, E1, F3, G3, H2, I11 ) ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F1, G2, H1, I11 ) ( A1, B1, C1, D1, E1, F2, G2, H1, I11 ) G: ( A1, B1, C3, D1, E1, F4, G8, H2, I11 ) ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) F: G1 G2 G3 ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) F1 13% 10% 10% ( A1, B1, C1, D1, E1, F2, G1, H1, I11 ) ( A1, B1, C2, D4, E1, F6, G3, H2, I11 ) F2 18% 20% 20% ( A1, B1, C3, D1, E1, F2, G3, H2, I11 ) F3 13% 7% 9% ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) F4 16% 9% 10% 20

  21. 4. Bayesian Network: visualization 21

  22. 4. Bayesian Network: visualization (2) condition on C1 22

  23. 4. Bayesian Network: visualization (3) condition on C2 23

  24. Evaluation: data ● Q1 2016 ● 3.5 billion IPs ● DNS ● Traceroutes ● CDN logs 24

  25. Evaluation: data ● Q1 2016 ● 3.5 billion IPs ● DNS ● Traceroutes ● CDN logs 25

  26. Evaluation: data ● Q1 2016 ● 3.5 billion IPs ● DNS ● Traceroutes ● CDN logs 26

  27. Aggregates 27

  28. Aggregates 28

  29. Aggregates 29

  30. Aggregates 30

  31. Evaluation: R1 (routers, global Internet carrier) 31

  32. R1 (routers) 32

  33. A. B. C. D Routers (brief) 33

  34. Evaluation: S4 (servers, leading cloud operator) 34

  35. S4 (servers) 35

  36. Servers (brief) 36

  37. Evaluation: C1 (clients, large mobile operator) 37

  38. C1 (clients) 38

  39. Clients (brief) 39

  40. Application: generating candidate targets ( A1, B1, C1, D1, E1, F1, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) ( A1, B1, C2, D2, E1, F5, G4, H2, I11 ) ( A1, B1, C2, D3, E1, F3, G3, H2, I11 ) ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F2, G3, H1, I11 ) ( A1, B1, C1, D1, E1, F1, G2, H1, I11 ) ( A1, B1, C1, D1, E1, F2, G2, H1, I11 ) G: ( A1, B1, C3, D1, E1, F4, G8, H2, I11 ) ( A1, B1, C1, D1, E1, F1, G1, H1, I11 ) F: G1 G2 G3 ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) F1 13% 10% 10% ( A1, B1, C1, D1, E1, F2, G1, H1, I11 ) ( A1, B1, C2, D4, E1, F6, G3, H2, I11 ) F2 18% 20% 20% ( A1, B1, C3, D1, E1, F2, G3, H2, I11 ) F3 13% 7% 9% ( A1, B1, C1, D1, E1, F1, G8, H1, I11 ) F4 16% 9% 10% 40

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