The Sociology of Sybils:
Understanding Social Network-based Sybil Defenses
Krishna P. Gummadi
Networked Systems Research Group
MPI-SWS
Sybil attack
• A fundamental problem in distributed systems
• Attacker creates many fake/sybil identities
• Many cases of real world attacks : Digg, Youtube
Automated sybil attack on
Youtube for $147!
Sybil defense
• Using a trusted central authority
– Tie identities to actual human beings
• Not always desirable
– Can be hard to find such authority
– Sensitive info may scare away users
– Potential bottleneck and target of attack
• Hard without a trusted central authority
– Impossible unless using special assumptions [Douceur ’02]
– Resource challenges using CPU, b.w., memory are not sufficient
• Adversary can have much more resources than typical user
• Need some resource that is hard to obtain in abundance
– Links in a social network?
Leveraging social networks:
Basic insight
• Resource Constraint
honest
nodes
Sybil
nodes
– Bound on number of trust
relationships between
attackers and honest nodes
– Attacker cannot create
arbitrarily large # of edges
between honest nodes and
Sybil identities
• Assumption: edges
represent mutual trust
– E.g., colleagues, relatives in
real-world
– Not online friends!
Several proposals to leverage social nets
• All rely on detecting the topological features resulting
from the resource constraint
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–
–
–
–
–
–
SybilGuard [Sigcomm ’06]
SybilLimit [Oakland S&P ’08]
Ostra [NSDI ’08]
SybilInfer [NDSS ’09]
SumUp [NSDI ’09]
Whanau [NSDI ’10]
MobId [INFOCOM ’10]
Example: SybilGuard
The sub-graph of
honest nodes is fast
mixing
honest
nodes
sybil
nodes
Disproportionally small
cut separating honest
and Sybil nodes
Cannot search for such a cut using brute-force
How SybilGuard works:
Random walk intersection
• Verifier accepts a
suspect if the two
routes intersect
Verifier
Suspect
honest nodes
Random walk length w: ~
sybil nodes
n log n
– W.h.p., verifier’s route
stays within honest
region
– W.h.p., routes from two
honest nodes intersect
– # of accepted Sybils <
g*w
• g: # of attack edges
• w: random walk
length
Another example: SumUp
• A Sybil resilient vote aggregator
• A central party collects all votes and the social graph
• Goal: extract a subset of votes
– include at most a few votes from Sybils
– include most votes from honest users
Step 1: Designate a vote collector
Step 2: Use max-flow to collect votes
Step 2: Use max-flow to collect votes
Step 3: Assign appropriate link capacities
Summary: Sybil defense schemes
• A number of Sybil schemes already proposed
– More with each passing conference
• All schemes rely on two common assumptions
– Honest nodes: they are fast mixing
– Sybils: they do not mix quickly with honest nodes
• But, each relies on its own graph analysis algorithm
– E.g., back-traceable random walk intersection, bayesian
inference from modified random walks, max-flow between
nodes, betweenness centrality of nodes
Problem with state of the art
• Fast mixing assumption provides little insight
– Into how the schemes work
– Or what structural properties affect their effectiveness
• Neither does the evaluation of the Sybil algorithms
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–
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Lots of sensitive parameters that impact results
Each scheme evaluated on different data sets
Each scheme performs differently on different data sets
Evaluations assume different adversarial models
Rest of the talk
• Investigate several unanswered questions:
– How do the different schemes compare against each other?
• Do they all find Sybils similarly?
– What types of network structures are vulnerable to Sybil attacks?
– How prevalent are such structures in real-world social networks?
• And discuss their implications
Results summary
– How do the different schemes compare against each other?
• Do they all find Sybils similarly?
– All Sybil schemes work by detecting tightly-knit node communities
– What types of network structures are vulnerable to Sybil attacks?
– When all honest nodes do not form a single cohesive community
– How prevalent are such structures in real-world social networks?
– Very prevalent! Real-world social communities have bounded size
Communities in social networks
• Group of users more densely connected than overall graph
Results summary
– How do the different schemes compare against each other?
• Do they all find Sybils similarly?
– All Sybil schemes work by detecting tightly-knit node communities
– What types of network structures are vulnerable to Sybil attacks?
– When all honest nodes do not form a single cohesive community
– How prevalent are such structures in real-world social networks?
– Very prevalent! Real-world social communities have bounded size
How Sybil defense schemes work
• At their core, Sybil schemes partition the network
– Into Sybils and non-Sybils
• Partitioning algorithms can be viewed as ranking nodes
– With a sliding cutoff determined by parameters
How Sybil defense schemes work
• Ranking is independent of an algorithm’s parameters
• Changing parameters yields different partitions
Comparing Sybil defense schemes
• Compare their node rankings at different partitionings
– How do the partitions formed by the first k nodes compare
• Metric: Mutual information [Strehl ’02]
– Varies between 0 and 1
– 0 => no correlation between the partitionings
– 1 => perfect match
Comparing Sybil defense schemes
• All Sybil schemes
rank nodes in the
local community
before others
Toy topology with two well defined communities
• No correlation
between rankings
within or outside
local community
Comparing Sybil defense schemes
• Using a Facebook subgraph
– Nodes from local community ranked before others
– Little correlation between rankings within & outside the community
Comparing Sybil defense schemes
• Using an Astrophysicist network
– Nodes from local community ranked before others
– Little correlation between rankings within & outside the community
Summary:
Comparing Sybil defense schemes
• All node rankings are biased towards decreasing conductance
• When multiple nodes are similarly well connected, their
orderings can vary in different schemes
• Nodes in cohesive clusters around reference node are ranked
before others in all schemes
• Sybil defense schemes are effectively detecting communities!
Rest of the talk
• Investigate several unanswered questions:
– How do the different schemes compare against each other?
• Do they all find Sybils similarly?
– All Sybil schemes work by detecting tightly-knit node communities
– What types of network structures are vulnerable to Sybil attacks?
– How prevalent are such structures in real-world social networks?
• And discuss their implications
What networks are vulnerable to Sybil attacks?
• When non-Sybils are divided into multiple communities
– Cannot tell apart Sybils & non-Sybils in a distant community
– Attackers can launch very effective targeted attacks
Do non-Sybils form multiple communities?
• Some real-world social networks have high modularity
– They exhibit well defined community structures
Are networks with stronger community
structures more vulnerable?
• Yes! Networks with higher modularity are more
susceptible to attacks
– Independent of the Sybil defense scheme used
Rest of the talk
• Investigate several unanswered questions:
– How do the different schemes compare against each other?
• Do they all find Sybils similarly?
– All Sybil schemes work by detecting tightly-knit node communities
– What types of network structures are vulnerable to Sybil attacks?
– When all honest nodes do not form a single cohesive community
– How prevalent are such structures in real-world social networks?
• And discuss their implications
How often do non-Sybils form one
cohesive community?
• Traditional methodology:
– Analyze several real-world social network graphs
– Generalize the results to the universe of social networks
• A more scientific method:
– Leverage insights from sociological theories on communities
– Test if their predictions hold in online social networks
– And then generalize the findings
Group attachment theory
• Explains how humans join and relate to groups
• Common-identity based groups
– Membership based on self interest or ideology
– E.g., NRA, Greenpeace, and PETA
– Tend to be loosely-knit and less cohesive
• Common-bond based groups
– Membership based on inter-personal ties, e.g., family or kinship
– Tend to form tightly-knit communities within the network
Dunbar’s theory
• Limits the # of stable social relationships a user can have
– To less than a couple of hundred
– Linked to size of neo-cortex region of the brain
• Observed throughout history since hunter-gatherer societies
• Also observed repeatedly in studies of OSN user activity
– Users might have a large number of contacts
– But, regularly interact with less than a couple of hundred of them
• Limits the size of cohesive common-bond based groups
Prediction and implication
• Strongly cohesive communities in real-world social
networks will be necessarily small
– No larger than a few hundred nodes!
• If true, it imposes a limit on the number of non-Sybils
we can detect with high accuracy
– Will be problematic as social networks grow large
Verifying the prediction
• In all networks,
groups larger than a
few 100 nodes do
not remain cohesive
Real-world data sets analyzed
• Small cohesive
groups tend to be
family and alumni
groups
• Large groups are
often on abstract
topics like music or
politics
Rest of the talk
• Investigate several unanswered questions:
– How do the different schemes compare against each other?
• Do they all find Sybils similarly?
– All Sybil schemes work by detecting tightly-knit node communities
– What types of network structures are vulnerable to Sybil attacks?
– When all honest nodes do not form a single cohesive community
– How prevalent are such structures in real-world social networks?
– Very prevalent! Real-world social communities have bounded size
• And discuss their implications
Implications
• Fundamental limits on social network-based Sybil defenses
• Can reliably identify only a limited number of honest nodes
• In large networks, limits interactions to a small subset of
honest nodes
– Might still be useful in certain scenarios, e.g., white listing email
from friends
• Social network-based Sybil defense is a misnomer!
Future directions
• Leverage information beyond social network structure
– E.g., inter-user activity can reveal the strength of ties and
help eliminate links to Sybils
• Move towards Sybil tolerance
– Rather than preventing users from creating multiple identities
– Focus on limiting privileges
Summary
• We discussed social network-based Sybil defenses
• Lots of proposed schemes, but little understanding
– Of how they compare with each other
– Or what structural properties impact them
– Or how well they would work in real-world social networks
• We found that Sybil schemes
– Work by effectively detecting communities
– Are vulnerable in networks with well defined community structures
– Can find only a limited number of trustworthy nodes in real-world
• Our findings suggest that we need to move beyond using
only the social network to defend against Sybil attacks
Thanks!
Questions?
• Acknowledgements:
– Joint work with Bimal Viswanath, Ansley Post, and Alan
Mislove
– Thanks to Haifeng Yu and Nguyen Tran for illustrations of
SybilGuard and SumUp Sybil defense schemes