Collusion-Resistance Misbehaving User Detection Schemes Speaker: Jing-Kai Lou 2015/10/131.
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Transcript of Collusion-Resistance Misbehaving User Detection Schemes Speaker: Jing-Kai Lou 2015/10/131.
Collusion-Resistance Misbehaving User Detection Schemes
Speaker: Jing-Kai Lou
112/04/19 1
Outline
• Introduction–What’s the problem– Does it matter
• Previous work: What have I done …– Community-based scheme
• Current Analysis: What am I doing …–HITS– Random walk scheme
112/04/19 2
The Rise of User Generated Content
• Most of the fastest-growing sites on the internet now are based on user-generated content (UGC).
Customer Reviews Increase Web Sales
--- eMarketer112/04/19 3
Inappropriate UGC
• The misbehaving users– post the inappropriate UGC
• Hiring lots of official moderators– is the typical solution
• But, such high labor cost is a great burden to the service provider
• There is another choice …
112/04/19 4
Social Moderation System
• A user-assist Moderation• Every user is a reviewer
Blogger
Blogger
Album
Album VideoVideo
??????
!?!?
OO XXXX
Official moderator inspects what you see
You report what you see while viewing
XX
112/04/19 5
Social Moderation Effect
• Advantages of social moderation system:1.Fewer official moderators2.Detecting inappropriate content quickly
• The number of the reports is still large.1% uploading photos in Flickr are problematic, there are still about 43,200 reports each day
• An automation scheme to filter the reports
112/04/19 6
Automated Filter for Reports
• Sorting the reports by their number of accusations
37473
These photos are reported no more than (N =20) times
These photos are reported more than (N =20) times112/04/19 7
However, the collusion exists…112/04/19 8
Not All Users Are Trustable
• While most users report responsibly, While most users report responsibly, colluders report fake results colluders report fake results to to gain some benefitsgain some benefits
112/04/19 9
The Objective
• To develop a collusion-resistant scheme
• CAN automatically infers whether the accusations are fair or malicious.
The scheme, therefore, distinguish
misbehaving users from victims.112/04/19 10
Our Work: Graph Theory Approach
• Using the report (accusation) relation only
• Previous work: Community-based Scheme– Submitted to 3rd ACM workshop on
Scalable Trust Computing (STC 2008)
• Extended work: – Propose new schemes– Analyzing new schemes…
112/04/19 11
COMMUNITY-BASED SCHEMECOMMUNITY-BASED SCHEME
112/04/19 12
Community-based Scheme
• Achieving accuracy rate higher than 90%
• Preventing at least 90% victims from collusion attack
112/04/19 13
Idea of Community-based Scheme
• Accusation Relation: Accusing Graph:
1 2 3 4 5
1 0 1 0 0 0
2 0 0 0 1 0
3 0 1 0 0 1
4 0 0 0 0 0
5 0 1 0 0 0
112/04/19 14
Ideal Patterns
112/04/19 15
Colluder
Victim
Normal user
Misbehaving user
Accusing Community
• Users with similar accusing tend to be in the same community
112/04/19 16
Inter-community edge
Designing Features for Each User
• To find accusations NOT from colluders• Base on the communities, we design
features
– Incoming Accusation, IA(k) = 2,
–Outgoing Accusation, OA(k) = 5
k
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Community-based Algorithm
1. Partitioning accusing graph into communities.
2. Computing the feature pair (IA, OA) of each user
3. Clustering based on their (IA, OA) pairs, and label users in the cluster with large (IA, OA) as misbehaving users.
112/04/19 18
Evaluation Metric
• What we care is, False Negative–Misidentifying victims as misbehaving
users
• Collusion Resistance
112/04/19 19
Effect of #(Misbehaving users)
Ou
r Meth
od
Cou
nt-b
ased
Meth
od
112/04/19 20
Effect of #(Colluders)
Ou
r Meth
od
Cou
nt-b
ased
Meth
od
112/04/19 21
Effect of Accusation Density
Ou
r Meth
od
Cou
nt-b
ased
Meth
od
112/04/19 22
Weakness of Community-based scheme
• In our simulation, the colluders only accuse the victims.
• Realistically, the colluders sometimes may also vote some misbehaving users.
• We shall consider smart colluder
112/04/19 23
Smart Colluder Behavior
• Behavior :=probability for colluder to vote misbehaving users, ranges from 0 to 100.
Behavior
0 100
Naïve Colluder
Smart ColluderNormal user
112/04/19 24
HITS, HITS, HYPERLINK-INDUCED TOPIC SEARCHHYPERLINK-INDUCED TOPIC SEARCH
112/04/19 25
Inspiration
• A link analysis algorithm that rates Web pages, developed by Jon Kleinberg.
• It determines two values for a page: – its authority, which estimates the
value of the content of the page, – and its hub value, which estimates the
value of its links to other pages.
112/04/19 26
Ideal
• Authority Victim• Hub value Colluder
• For example, –Number of User = 150–Misbehaving User Ratio = 10%, i.e., 15– Colluder Ratio = 20%, i.e., 30– Behavior = 20%
112/04/19 27
112/04/19 28
When Behavior is increasing
• Parameter:–Number of User = 150–Misbehaving User Ratio = 10%, i.e., 15– Colluder Ratio = 20%, i.e., 30– Behavior = 50%
112/04/19 29
112/04/19 30
RANDOM WALK SCHEMERANDOM WALK SCHEME
112/04/19 31
Main Idea
1. Focusing on content accused by many reviewers
2. Creating undirected graph C to describe them and their relation
3. Shaping C, (named it as D) to satisfy the Goal
4. Goal:Putting many people walking several steps on D, then most of people would stay on “victims” finally
112/04/19 32
Co-Voter Graph, C
• Define a co-voter graph C(V, E) to describe the relation between all accused
• V(G): accused• E(G):– if the intersection of accusers against
accused i and j (vertex i and j), then (i, j) in E(G)
–weight, w(i. j) = #(intersection of accusers)
112/04/19 33
A snap shot of co-voter graph
B
C
A
E
F
D
1, 2, 3, 4, 5, 6, 7, 81, 12, 13, 14 5,6,7,8
5, 7,81, 2, 4, 8, 9, 10 5, 6, 7112/04/19 34
Making Ideal Tendency (Be Directed)
M
M’
V
V’
FORCE
321
Strong
Weak
GOAL:1.For M, 2 > 12.For V, 3 > 2
112/04/19 35
Key Node Key Node
Goal 1: Intersection Ratio
M
M’
V
112/04/19 36
Prob. to V
Prob. to M
GOAL 2: Alpha of Target
• Alpha(M) < Alpha(V), hopefully
M b
V
112/04/19 37
Prob. to M = Alpha(M)Prob
. to V
= A
lph
a(V
)
What should be Alpha?
• [Version N(eighborhood)]: Alpha(T) := the number of co-voters between b and all its neighbors
Colluder tend to share more co-voters with his collusion group …
• [Version H(ub)]: Alpha(T) := Sum(hub score of T’s voter)
112/04/19 38
Weight Formula Options
• Directed weight formula: w(a, b) =Alpha(b) * |a intersect b| / |a union b|
• Then, we set the node leaving prob. by normalizing outgoing weight
112/04/19 39
X0.4
0.80.8
A
BC
Pr(X A) = .4Pr(X B) = .2Pr(X C) = .4
Evaluation
• Parameter:–Number of User = 250–Misbehaving User Ratio = 10%, i.e., 25– Colluder Ratio = 20%, i.e., 50– Behavior = 50%
112/04/19 40
Evaluation
112/04/19 41
Conclusion
• Any new factor we shall consider?• Any idea to improve the random
walk scheme, or HITS Scheme?• Any NEW idea?
112/04/19 42
THANKS FOR YOUR LISTENING!
112/04/19 43