Report:鄭志欣

Conference:Brett Stone-Gross, Marco Cova, Lorenzo Cavallaro, Bob Gilbert, Martin Szydlowski, Richard Kemmerer, Chris Kruegel, and Giovanni Vigna, in Proceedings of the ACM CCS, Chicago, IL, November 2009.

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Date Collect : 2009/1/25 ~ 2009/2/5

180’000 infections

70GB data

USD$ 83,000 ~ 8,300,000 (bank account and credit card)

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Introduction Botnet Analysis Threats and data analysis Conclusion

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The main purpose of this paper is to analyze the Torpig botnet’s operations.• Botnet size.• The personal information is stolen by

botnets.

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Torpig solves fast-flux by using a different technique for locating its C&C servers, which we refer to as domain flux.

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Data Collection and Format

Submission Header

Botnet Size vs. IP Count

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Date : 70GB (10 day)

Protocol : HTTP POST requests

Submission Header VS. Request body

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Ts = time stamp IP Sport = SOCKS proxies port Hport = HTTP port OS = operation system version Cn = locale Nid = bot identifier Bld and ver = build and version number of Torpig

gh5

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Counting Bots by Submission Header Fields

(nid , os , cn , bld , ver) decide to unique bot

Delete Probers and Researcher

18200 hosts

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4690 Bots / hour

705 Bots / hour

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DHCP (ISPs recycles IPs)

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Financial Data Stealing

Password Analysis

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In ten days, Torpig obtained the credentials of 8,310 accounts at 410 different institutions. The top targeted institutions were PayPal (1,770 accounts), Poste Italiane (765), Capital One (314), E*Trade (304), and Chase (217).

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we found that a naïve evaluation of botnet size based on the count of distinct IPs yields grossly overestimated results.

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