collusion-resistance misbehaving user detection schemes speaker: jing-kai lou 2015/10/131

Collusion-Resistance Misbehaving User Detection Schemes

Speaker: Jing-Kai Lou

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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

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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 …

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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

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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

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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

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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…

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COMMUNITY-BASED SCHEMECOMMUNITY-BASED SCHEME

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Community-based Scheme

• Achieving accuracy rate higher than 90%

• Preventing at least 90% victims from collusion attack

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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

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Ideal Patterns

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Colluder

Victim

Normal user

Misbehaving user

Accusing Community

• Users with similar accusing tend to be in the same community

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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.

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Evaluation Metric

• What we care is, False Negative–Misidentifying victims as misbehaving

users

• Collusion Resistance

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Effect of #(Misbehaving users)

Ou

r Meth

od

Cou

nt-b

ased

Meth

od

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Effect of #(Colluders)

Ou

r Meth

od

Cou

nt-b

ased

Meth

od

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Effect of Accusation Density

Ou

r Meth

od

Cou

nt-b

ased

Meth

od

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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

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Smart Colluder Behavior

• Behavior :=probability for colluder to vote misbehaving users, ranges from 0 to 100.

Behavior

0 100

Naïve Colluder

Smart ColluderNormal user

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HITS, HITS, HYPERLINK-INDUCED TOPIC SEARCHHYPERLINK-INDUCED TOPIC SEARCH

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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.

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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%

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When Behavior is increasing

• Parameter:–Number of User = 150–Misbehaving User Ratio = 10%, i.e., 15– Colluder Ratio = 20%, i.e., 30– Behavior = 50%

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RANDOM WALK SCHEMERANDOM WALK SCHEME

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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

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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)

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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

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Key Node Key Node

Goal 1: Intersection Ratio

M

M’

V

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Prob. to V

Prob. to M

GOAL 2: Alpha of Target

• Alpha(M) < Alpha(V), hopefully

M b

V

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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)

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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

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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%

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Evaluation

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Conclusion

• Any new factor we shall consider?• Any idea to improve the random

walk scheme, or HITS Scheme?• Any NEW idea?

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THANKS FOR YOUR LISTENING!

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