Peeling Onions

AVTOKYO 2015 発表資料

Transcript

1. Peeling Onions @ntddk AVTOKYO 2015 2015.11.14 1
2. $ whoami • http://ntddk.github.io • Student • Security Camp Lecturer • CODE BLUE 2015 Speaker 2
3. Peeling Onions 3
4. The Onion Routing 4
5. Tor • • One of the most common anonymous communication systems 5
6. Tor 6 AES encryption
7. Tor 7 Exit node can sniff plaintext packet. So you should use SSL. IP Entry node can knows source IP address.
8. Tor 8 Tor hidden services Hidden services can only be accessed through Tor Exit nodes are not used at this time. .onion TLD These are also called “deep web”.
9. 9 http://blog.trendmicro.co.jp/archives/12349 Investigation of underground community in Japan
10. 10 https://www.facebookcorewwwi.onion Hash of public key
11. 11 https://www.facebookcorewwwi.onion
12. Tor 12
13. 13 https://goo.gl/PFWpYn
14. 14 Tor Measurement of drug marketplace on Tor network https://www.usenix.org/conference/usenixsecurity15/technical- sessions/session/measurement
15. 15 Confidence is most important in underground. One review, one transaction.
16. 16
17. Crawling Deep Web 17
18. 18 http://securelist.com/blog/incidents/58542/tor-hidden-services-a-safe-haven-for- cybercriminals/ 900 hidden services Kaspersky found 900 hidden services.
19. 19 http://securelist.com/blog/incidents/58542/tor-hidden-services-a-safe-haven-for- cybercriminals/ Tor 30,000 hidden services Tor project says there are over 30,000 hidden services.
20. 20 https://bdpuqvsqmphctrcs.onion List of 174,523 hidden services
21. 21 { "aaData": [ [ "0", "torlinkbgs6aabns.onion", "TorLinks | .onion Link List The Hidden Wiki TheHiddenWiki Onion Urls onionland Tor linklist Deepweb", "https://encrypted.google.com/search?q=¥".onion¥"", "1442342899", "1369353102", "388", "2328" ], [ "1", "ci3hn2uzjw2wby3z.onion", "Talk.onion", "https://encrypted.google.com/search?q=¥".onion¥"", "1375548844", "1369353102", "396", … But, how many of them still alive?
22. Scraping • • Confirm the existence of services from the list. 22 torsocks wget ¥ --connect-timeout=10 --tries=1 ¥ --user-agent= ¥ “Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; rv:8.0.1) Gecko/20100101Firefox/8.0.1” ¥ [.onion] Same as Tor browser html You should scrape only html to avoid child pornography
23. Scraping • • Confirm the existence of services from the list. • 4,102/174,523 • Found 4,102/174,523 sites still alive! 23 torsocks wget ¥ --connect-timeout=10 --tries=1 ¥ --user-agent= ¥ “Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; rv:8.0.1) Gecko/20100101Firefox/8.0.1” ¥ [.onion]
24. Word Cloud • html • Now we have html files of the top pages. • • Visualize word frequency 24 #!/usr/bin/env python2 import sys from os import path from wordcloud import WordCloud d = path.dirname(__file__) argvs = sys.argv text = open(path.join(d, argvs[1])).read() wordcloud = WordCloud(max_font_size=600,width=2560,height=1440).generate(text) wordcloud.to_file(path.join(d, argvs[1]+".png"))
25. 25 • Word cloud of all sites
26. 26 • Word cloud of sites which contain “malware” • • Black hat hacker likes sophomoric words. lol
27. Clustering • html • Strip html tags, extract text 27
28. Clustering • Calculate tf-idf • • Term frequency • • Inverse document frequency • tf*log_2(N/df) 28 0.0896242738109923 facebook 0.0811608402477274 checkthis 0.0763369146951766 words 0.0637879047442443 sign 0.0599923671251114 039 0.0494245419919427 try https://www.facebookcorewwwi.onion
29. Clustering • Pearson score 29 a b c d e
30. Clustering • 100 sites (randomly chosen) 30
31. Clustering • 1,000 sites (randomly chosen) 31 __ ━┓ ― ┏┛ ( ) ( ) ⌒ __ | / _ / | | | |
32. Clustering • 4,102 sites (all) • • O(N^2) 32
33. 33
34. Tor shops • PHP • Template written in PHP for black markets • Bitcoin • 48 • Used at 48 sites 34
35. Clustering (Tor shops) 35
36. Clustering (Tor shops) 36
37. Clustering (Tor shops) 37
38. Clustering (Tor shops) 38
39. Clustering (Tor shops) 39
40. Clustering (Tor shops) 40
41. Clustering (Tor shops) 41
42. Clustering (Tor shops) 42
43. Clustering (Tor shops) 43
44. Clustering (Tor shops) 44
45. Clustering (Tor shops) 45
46. Clustering (Tor shops) 46
47. Clustering (Tor shops) 47
48. Clustering (Tor shops) 48
49. Clustering (Tor shops) 49
50. Clustering (Tor shops) • About 12 clusters • Hitman, Drug, Phone, Tablet, Kush, LSD, Cannabis, Cocaine, USD, Hacker, US Passport, UK Passport 50
51. 51 https://www.youtube.com/watch?v=-oTEoLB-ses&feature=youtu.be&t=1998 Previous research of clustering web-based hidden services But not focused on black markets
52. Conclusion • Tor hidden services • There are hidden services for criminal purpose. • 12 • There are about 12 types of black markets. • k-means • We will do further analysis by using a k-means in the future. 52
53. Running Exit Node 53
54. Tor 54 Exit node can sniff unencrypted packet. So you should use SSL. IP Entry node can knows source IP address.
55. Tor 55 Exit node can sniff unencrypted packet. Tor We can monitor attacks via Tor.
56. • • For victim, attacker is YOU. • IPS • IPS is required. 56
57. 57
58. Attack to exit node • .torrc • You can specify exit nodes by .torrc. • 58
59. Pass the back (bad idea) 59 Tor Passing to Tor once more. Entry node
60. Appendix 60
61. 61 https://goo.gl/PFWpYn
62. 62 https://goo.gl/PFWpYn
63. 63 https://goo.gl/PFWpYn
64. 64 https://goo.gl/PFWpYn
65. 65 https://www.reddit.com/r/SilkRoad/
66. 66 https://www.reddit.com/r/DarkNetMarkets
67. 67 “The Dark Net: Inside the Digital Underworld” ( )
68. 68 Session of anonymizing data https://www.usenix.org/conference/usenixsecurity15/technical-sessions/session/forget- me-not BGP/AS Tor BGP/AS-level de-anonymization of Tor Fingerprinting hidden service connections
69. 69 http://panopticlick.eff.org Tor browser on this machine(default setting)
70. 70 https://torstatus.blutmagie.de There are only ~7000 exit nodes Tor is not fully secure. Tor Tor Research is not in the stage about whether it can be de-anonymized or not. It is just about its efficiency.
71. • 187 deep web architectures • Alternative internet alter the world, crime as well. 71 https://github.com/redecentralize/alternative-internet