SybilGuard:%20Defending%20Against%20Sybil%20Attacks%20via%20Social%20Networks

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SybilGuard Defending Against Sybil Attacks via
Social Networks

• Haifeng Yu
• Michael Kaminsky
• Phillip B. Gibbons
• Abraham Flaxman
• Presented by
• John Mak, Janet Yung

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Outline

• Introduction to Sybil Attack
• Model Problem formulation
• Sybil Guard Overview
• Simulation Result Analysis
• Conclusion
• Our views

3
Introduction to Sybil Attack

• P2p and decentralized, distributed systems
  particularly vulnerable
• Malicious user obtains multiple fake identities
• Gain large influence by out vote honest users

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Introduction to Sybil Attack

• Malicious user obtains multiple fake identities
• Malicious behavior becomes a norm (e.g. Byzantine
  failures)
• Many protocols assume lt 1/3 malicious nodes
• Easily create 1/3 nodes ? Break defense

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Introduction to Sybil Attack

• Centralized authority
• Control Sybil attack easily
• Verify real life credential
• Hard for worldwide to trust
• Single point of failure bottleneck, DOS
• Scare away potential users requires sensitive
  information

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Introduction to Sybil Attack

• Decentralized approach is hard
• Harvest (Steal) IP addresses
• No common IP prefix ? Hard to filter
• Advertise BGP route on unused block of IP address
• Botnet - Co-opt large number of end-user machines

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Introduction to Sybil Attack

• Not very successful defense approaches
• Resource-challenge approach (computational
  puzzles)
• Network coordinates
• Reputation system based on historical behavior

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Model Problem Formulation

• Users
• n honest users single identity
• 1 malicious user multiple identities
• Identity
• Also called node
• Sybil identity malicious users identity
• Defense system
• Verifier node V accept another node S
• Ideally, V only accept honest nodes.

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Model Problem Formulation

• Bounding no. of sybil groups
• Divide all nodes into at most g equivalence
  groups
• Sybil Group equivalence group contains at least
  one Sybil node
• Defense system guarantees no. of groups, without
  need to know if the group is sybil

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Model Problem Formulation

• Bounding size of Sybil Group
• at most w nodes in a group
• limit no. of sybil nodes accepted each node can
  accept
• Summary
• decentralized
• honest node accepts, and is accepted by most
  other honest nodes
• honest node accepts a bounded number of sybil
  nodes.

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Social Network

• Consists of users (nodes)
• Human established trust relationships
• Nodes connected by an edge (friend)
• Real life relationship can bound both the number
  and size of sybil groups
• Usually degree 30
• Malicious user fools honest user to trust him/her
• an attack edge connected a malicious user and an
  honest user

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SybilGuard Overview

• Ensures honest user share at most one edge with
  sybil nodes created by a malicious user
• A protocol enables honest nodes to accept a large
  fraction of the other honest nodes
• SybilGuard does not increase or decrease the
  number of edges in the social network as a result
  of its execution

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SybilGuard Overview

• Random routes direct random walk for all nodes
• Pre-computed random permutation
• one-to-one mapping from incoming edges to
  out-going edges
• Random routes
• convergence property
• back-traceable property
• Multiple random routes of a certain length (w)

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Random route

• Basis of SybilGuard
• Honest node (verifier) decides whether or not to
  accept another node (suspect)
• Honest nodes random route
• highly likely to stay within the honest region
• Highly likely to intersect within (w) steps
• If there are (g) attack edges, the number of
  sybil groups is bounded by (g)

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Fast mixing property

• Assume social networks tend to be fast mixing,
  which necessarily means that subsets of honest
  nodes have good connectivity to the rest of the
  social network
• Assume the verifier is itself an honest node

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Attack edge
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Key exchange

• Each pair of friendly nodes shares a unique
  symmetric secret key (password) called the edge
  key
• Key distribution is done out-of-band
• Each honest node constrains its degree within
  some constant (e.g. 30) in order to prevent the
  adversary from increasing the number of attack
  edges (g) dramatically

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Limits attack edges

• Limited number of attack edges (g)
• Adding new attack edge needs out-of-band
  verification
• Malicious users
• Hard to convince honest users to be friends
• Quite difficult to do on a large scale

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Common ways adversary may use to increase g

• Befriending with honest users in real life
• Convince honest node to accept sybil nodes as
  friends
• Compromises a large fraction of nodes in the
  system.
• The adversary does not even need to launch a
  sybil attack. SybilGuard will not help here.
• Botnet
• Challenging to acquire a botnet containing many
  nodes that already in the system.

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Random route

• Convergence property
• Once two routes traverse the same edge along the
  same direction, they will merge and stay merged
  (i.e. the convergence property)
• Back-traceable property
• Using a permutation as the routing table further
  guarantees that the random routes are
  back-traceable
• There can be only one route with length (w) that
  traverses the same section of route (e)

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Problems of random route

• Loop (same edge more than once)
• Unlikely to form in a fast mixing graph
• Enters the sybil region
• Unlikely according toTHEOREM 1. For any
  connected and non-bipartite social network, the
  probability that a length-w random walk starting
  from a uniformly random honest node will ever
  traverse any of the g attack edges is upper
  bounded by gw/n. In particular, when w
  T(vnlogn) and g o(vn/logn), this probability is
  o(1).

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SybilGuard Design

• Use redundancy
• Instead of performing one random route
• A node with degree (d) performs random routes
  along each of its edges
• Verifier V accept suspect S
• If exist d/2 routes from the verifier node
• One of Vs route accept S if that route intersect
  with one of Ss route

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Registry table

• Each node will maintain and propagate ones
  registry tables and witness tables to its
  neighbors
• SybilGuard requires a node to register with all
  (w) nodes along each of its routes by using
  public key cryptography
• When a verifier V wants to verify S, V will ask
  the intersection point between Ss route and Vs
  route whether S is indeed registered

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Registry Witness tables
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Bandwidth consumption

• Studying a one million nodes social network
• w2000
• Data sent by each node for registry table is
  small
• Bandwidth consumption acceptable
• since the registry table updates are needed only
  when social trust relationships change

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Witness table

• Propagated and updated in a similar fashion as
  the registry table
• Backward
• Updated when a nodes IP address changes
• Can be done lazily in the verification process

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Verify process

• For a node V to verify a node S
• find the intersection nodes for all of its routes
  by the witness tables downstream
• Authenticates the intersection node one by one by
  the private key
• Ask that node to check if Ss public key is store
  in one of its registry tables.
• If Ss public key is found, that route of V will
  accept S

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Verify Process

• If more than half of the route from V accept S,
• node V will accept node S
• V will interact will S later by request S to
  encrypt its message by its private key
• For the sybil nodes region with (g) attack edges,
• Polluted entries in registry tables bounded by
  gww/2
• still less than half of the total number of
  entries ndw
• even with gw tends to (n) with (d) being the
  degree of each node (d gt 2) and (n) being the
  total number of nodes

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Route length w

• Constraints
• Must be sufficiently small to ensure remains
  entirely within the honest region
• Must be sufficiently large to ensure that routes
  will intersect with high probability
• w related to n
• Challenging because we do not know n for a
  decentralized system

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Route length w

• Determine locally by sampling
• Node A performs short random walk (e.g. 10 hops)
  at node B
• Assume B is honest (with high probability)
• A checks no. of hops for intersection with their
  random routes
• A asks for the witness tables from B.
• Repeat above, calculate median value.

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Sybil Guard under Dynamics

• Bypass offline nodes
• V verify other node S
• Probably multiple intersection points
• V have at least one intersection point online
• Propagate registry witness tables
• User creation / deletion / ip address change
• Infrequent changes
• Lookahead route table
• Store information of next K hops

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Sybil Guard under Dynamics

• Incremental routing table maintenance
• Instead of re-create a new permutation
• Make changes in current permutation
• Add
• X1 ? X2 ? X3 ? X4 ? (insert at end)
• X1 ? X2 ? (insert here) ? X4 ? X3
• Delete 3
• Before X1 ? X2 ? X3 ? X4 ? X5
• After X1 ? X2 ? X5 ? X4

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Attacks Exploiting Node Dynamics

• Potential attacks under Node Dynamics
• Malicious user M change public key to key2
• Suppose D?A?B?C
• Suppose revoke key1
• Random routes along all directions
• Ds key3 will overwrite key2

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Probability of Intersection

• Kleinbergs synthetic social network model
• a million-node graph with average node degree of
  24

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Results with no Sybil Attackers

• Probability of random routes being loops
• Loop reduces effective length of random route
• Loop is very rare
• 99.3 of the routes do not form loops in their
  first 2500 hops

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Results with no Sybil Attackers

• Probably of honest node being accepted
• at least one intersection point online
• If at least 10 online/offline intersection points
  ? verification succeeds
• In 1 million-node graph
• w 300
• probability 99.96 having gt10 intersections

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Results with no Sybil Attackers

• Estimate random route length w
• Sampling technique to determine w
• Node A choose a node B to determine w
• Node B not necessarily uniformly random
• Need to re-estimate daily

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Probability of routes in honest region

• 1 million-node graph
• 100 for g lt2000 99.8 for g2500
• 0.2 -- Nodes befriending with sybil attackers

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Probability of honest nodes being accepted

• Still 99.8 with 2500 attack edges
• Redundancy is necessary

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Our views

• Hard to link real life to virtual network?
• My real life friends may not join the virtual
  network
• Maybe centralized authentication better?
• 99.8 honest nodes accepted, but 0.2 not
  accepted.
• The 0.2 is honest

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Others views

• Fast mixing assumption in social network
• Japaneses social network may not mix with US
  social network?