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Netflow and Botnets


									Netflow and Botnets

   Steven M. Bellovin
  Columbia University

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• Most hosts are either clients or servers
  – P2P traffic is an exception
• Bots talk to other bots and thus to command
  and control node
• By looking for unusual traffic flows – client-to-
  client traffic that isn’t P2P – we can find bots

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• Use Netflow data to identify clients and
• Classify nodes as clients or servers
• Build a traffic matrix from the data to see
  which clients talk to which other clients
• Exclude P2P traffic, which is generally
  identifiable based on flow size

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• Originally from Cisco; now implemented by
  most router vendors
  – Also an IETF “Proposed Standard”
• Records “flow information” – src/dst pairs
  (addresses and port numbers), length, timing,
  etc. – for “connections” through a given router
• Intended for accounting and for traffic

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        Problems with Netflow
• Flows are unidirectional; need two records for
  complete picture
  • This is a consequence of Internet topology; most
    inter-ISP connections follow asymmetric paths
• Routers often deliver sampled data; can miss
  flow start/end packets
• Does not give unambiguous indication of
  client versus server

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• Build tools at Columbia
  – Easy access to machines and data
• Use existing archive of CU netflow data
  – Unclear if there are botnets present; get classification
    right first
• Get other netflow archives (e.g., from
• Bring nominally-working code to AT&T to
  experiment with large-scale datasets
• Compare with previous results from AT&T as
  check on correctness
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            Node Classification
• Must use heuristics
  – Flag field in netflow data doesn’t show client vs.
  – Timestamp not useful because of sampling
• Current strategy: look at port number
  – Clients usually use ports 48K-64K
• Considering using node degree
  – But – problems with low-activity hosts?

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          Classification is Hard
• Simple heuristics have not been satisfactory
• Building visualization tools to help us
  understand the data

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Client: Port Number by Volume

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Client: Port Number Scatter Plot

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Server: Port Number by Volume

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Server: Port Number Scatter Plot

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Ambiguous Host

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Ambiguous Host Scatter Plot

      Is this the sort of host we’re looking for?
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               Current Status
• Have basic tools built
• Working with visualization tools to understand
  the data
• Next steps:
  – Refine classification algorithms
  – Confirm analysis of bots in sample data
  – Try tools on larger dataset

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