Real-world

Oriented

Information

Sharing

Using

Social

Networks

While

users

disseminate

various

information

in

the

open

and

widely

distributed

environment

of

the

Semantic

Web

determination

of

who

shares

access

to

particular

information

is

at

the

center

of

looming

privacy

concerns

We

propose

a

real-world

oriented

information

sharing

system

that

uses

social

networks

The

system

automatically

obtains

users

social

relationships

by

mining

various

external

sources

It

also

enables

users

to

analyze

their

social

networks

to

provide

awareness

of

the

information

dissemination

process

Users

can

determine

who

has

access

to

particular

information

based

on

the

social

relationships

and

network

analysis

H.

-LSB-

Information

Interfaces

AND

Presentation

-RSB-

Group

and

Organizational

Interfaces

collaborative

computing

computer-supported

cooperative

work

web-based

interaction

Design

Human

Factors

With

the

current

development

of

tools

and

sites

that

enable

users

to

create

Web

content

users

have

become

able

to

easily

disseminate

various

information

For

example

users

create

Weblogs

which

are

diary-like

sites

that

include

various

public

and

private

information

Furthermore

the

past

year

has

witnessed

the

emergence

of

social

networking

sites

that

allow

users

to

maintain

an

online

network

of

friends

or

associates

for

social

or

business

purposes

Therein

data

Permission

to

make

digital

or

hard

copies

of

all

or

part

of

this

work

for

personal

or

classroom

use

is

granted

without

fee

provided

that

copies

are

not

made

or

distributed

for

profit

or

commercial

advantage

and

that

copies

bear

this

notice

and

the

full

citation

on

the

first

page

To

copy

otherwise

to

republish

to

post

on

servers

or

to

redistribute

to

lists

requires

prior

specific

permission

and/or

a

fee

GROUP

November

Sanibel

Island

Florida

USA

Copyright

ACM

related

to

millions

of

people

and

their

relationships

are

publicly

available

on

the

Web

Although

these

tools

and

sites

enable

users

to

easily

disseminate

information

on

the

Web

users

sometimes

have

difficulty

in

sharing

information

with

the

right

people

and

frequently

have

privacy

concerns

because

it

is

difficult

to

determine

who

has

access

to

particular

information

on

such

applications

Some

tools

and

applications

provide

control

over

information

access

For

example

Friendster

a

huge

social

networking

site

offers

several

levels

of

control

from

public

information

to

only

for

friends

However

it

provides

only

limited

support

for

access

control

An

appropriate

information

sharing

system

that

enables

all

users

to

control

the

dissemination

of

their

information

is

needed

to

use

tools

and

sites

such

as

Weblog

Wiki

and

social

networking

services

fully

as

an

infrastructure

of

disseminating

and

sharing

information

In

the

absence

of

such

a

system

a

user

would

feel

unsafe

and

would

therefore

be

discouraged

from

disseminating

information

How

can

we

realize

such

an

information

sharing

system

on

the

Web

One

clue

exists

in

the

information

sharing

processes

of

the

real

world

Information

availability

is

often

closely

guarded

and

shared

only

with

the

people

of

one

's

social

relationships

Confidential

project

documents

which

have

limited

distribution

within

a

division

of

company

might

be

made

accessible

to

other

colleagues

who

are

concerned

with

the

project

Private

family

photographs

might

be

shared

not

only

with

relatives

but

also

with

close

friends

A

professor

might

access

a

private

research

report

of

her

student

We

find

that

social

relationships

play

an

important

role

in

the

process

of

disseminating

and

receiving

information

This

paper

presents

a

real-world

oriented

information

sharing

system

using

social

networks

It

enables

users

to

control

the

information

dissemination

process

within

social

networks

The

remainder

of

this

paper

is

organized

as

follows

section

describes

the

proposed

information

sharing

system

using

social

networks

In

section

we

describe

the

application

of

our

system

Finally

we

conclude

this

paper

in

section

Figure

depicts

the

architecture

of

the

proposed

information

sharing

system

The

system

functions

as

a

plug-in

for

applications

so

that

external

applications

enable

users

to

leverage

social

networks

to

manage

their

information

Social

network

analysis

Applications

for

Information

Sharing

e.g.

Weblog

Wiki

CMS

SNS

etc

Contents

Data

Access

control

List

Editor

Access

control

Data

-LRB-

XACML

-RRB-

Social

networks

extraction

Web

Web

pages

SNS

FOAF

etc

Email

Sensors

Social

networks

Editor

Social

networks

Data

-LRB-

FOAF

-RRB-

Edit

social

networks

Edit

access

control

list

Contents

data

Access

to

contents

Access

permission

User

data

Figure

Architecture

of

the

proposed

information

sharing

system

Birthplace

Kagawa

Japan

Workplace

AIST

Job

CS

researcher

Research

topics

Web

University

Tokyo

univ.

Interest

Sumo

wrestling

person

Properties

Birthplace

Los

Angels

US

Workplace

Washington

Univ.

Job

CS

researcher

Research

topics

Web

University

UC

California

Interest

Sumo

wrestling

person

Properties

Birthplace

Kagawa

Japan

Workplace

AIST

Job

CS

researcher

Research

topics

Web

University

Tokyo

univ.

Interest

Sumo

wrestling

person

Properties

Birthplace

Los

Angels

US

Workplace

Washington

Univ.

Job

CS

researcher

Research

topics

Web

University

UC

California

Interest

Sumo

wrestling

person

Properties

person

person

Event

Participate

Common

Event-participation

relationship

Common

property

relationship

Figure

Two

kinds

of

relationships

semination

A

user

can

attach

an

access

control

list

to

his

content

using

his

social

network

when

creating

content

on

an

application

Then

when

the

application

receives

a

request

to

access

the

content

it

determines

whether

to

grant

the

request

based

on

the

access

control

list

Because

users

determine

the

access

control

to

information

based

on

the

social

network

the

system

requires

social

network

data

The

system

obtains

users

social

networks

automatically

by

mining

various

external

sources

such

as

Web

emails

and

sensor

information

subsequently

it

maintains

a

database

of

the

social

network

information

Users

can

adjust

the

network

if

necessary

The

system

enables

users

to

analyze

their

social

network

to

provide

awareness

of

the

information

dissemination

process

within

the

social

network

Using

social

relationships

and

the

results

of

social

network

analyses

users

can

decide

who

can

access

their

information

Currently

the

proposed

system

is

applied

to

an

academic

society

because

researchers

have

various

social

relationships

-LRB-

e.g.

from

a

student

to

a

professor

from

a

company

to

a

university

-RRB-

through

their

activities

such

as

meetings

projects

and

conferences

Importantly

they

often

need

to

share

various

information

such

as

papers

ideas

reports

and

schedules

Sometimes

such

information

includes

private

or

confidential

information

that

ought

only

to

be

shared

with

appropriate

people

In

addition

researchers

have

an

interest

in

managing

the

information

availability

of

their

social

relationships

The

information

of

social

relationships

of

an

academic

society

in

particular

computer

science

is

easily

available

online

to

a

great

degree

Such

information

is

important

to

obtain

social

networks

automatically

Hereafter

we

explain

in

detail

how

social

networks

are

modeled

extracted

and

analyzed

Then

we

explain

how

users

can

decide

to

control

information

access

using

social

networks

Representation

of

Social

Relationships

With

the

variety

of

social

relationships

that

exist

in

the

real

world

a

salient

problem

has

surfaced

integration

and

consolidation

on

a

semantic

basis

The

representation

of

social

relationships

must

be

sufficiently

fine-grained

that

we

can

capture

all

details

from

individual

sources

of

information

in

a

way

that

these

can

be

recombined

later

and

taken

as

evidence

of

a

certain

relationship

Several

representations

of

social

relationships

exist

For

example

social

network

sites

often

simplify

the

relationship

as

friend

or

acquaintance

In

the

Friend

of

a

Friend

-LRB-

FOAF

-RRB-

-LSB-

-RSB-

vocabulary

which

is

one

of

the

Semantic

Web

's

largest

and

most

popular

ontologies

for

describing

people

and

whom

they

know

many

kinds

of

relationships

between

people

are

deliberately

simplified

as

knows

relations

A

rich

ontological

consideration

of

social

relationships

is

needed

for

characterization

and

analysis

of

individual

social

networks

We

define

two

kinds

of

social

relationship

-LRB-

Fig.

-RRB-

-LSB-

-RSB-

The

first

basic

structure

of

social

relationship

is

a

person

's

participation

in

an

event

Social

relationships

come

into

existence

through

events

involving

two

or

more

individuals

Such

events

might

not

require

personal

contact

but

they

must

involve

social

interaction

From

this

event

social

relationships

begin

a

lifecycle

of

their

own

during

which

the

characteristics

of

the

relationship

might

change

through

interaction

or

the

lack

thereof

An

event

is

classified

as

perdu-rant

in

the

DOLCE

ontology

-LSB-

-RSB-

which

is

a

popular

ontology

For

example

an

event

might

be

a

meeting

a

conference

a

baseball

game

a

walk

etc.

Assume

that

person

A

and

person

B

participate

in

Event

X

In

that

situation

we

note

that

A

and

B

share

an

event

co-participation

relationship

under

event

X

A

social

relationship

might

have

various

social

roles

asso-ciated

with

it

For

example

a

student-professor

relationship

within

a

university

setting

includes

an

individual

playing

the

role

of

a

professor

another

individual

plays

the

role

of

a

student

If

A

and

B

take

the

same

role

to

Event

X

they

are

in

a

same

role

relationship

under

event

X

-LRB-

e.g.

students

at

a

class

colleagues

in

a

workspace

-RRB-

If

A

can

not

take

over

B

's

role

or

vice

versa

A

and

B

are

in

a

role-sharing

relationship

-LRB-

e.g.

a

professor

and

students

a

project

leader

and

staff

-RRB-

Another

kind

of

social

relationship

is

called

a

common

property

relationship

Sharing

the

same

property

value

generates

a

common

property

relationship

between

people

For

example

person

A

and

person

B

have

a

common

working

place

common

interests

and

common

experiences

Consequently

they

are

in

a

common

property

relationship

with

regard

to

those

common

properties

Extraction

of

Social

Networks

If

two

persons

are

in

either

an

event

co-participation

relationship

or

a

common

property

relationship

they

often

communicate

The

communication

media

can

be

diverse

Figure

Editor

for

social

relationships

Figure

Editor

for

analyzing

social

networks

and

assigning

an

access

control

list

to

content

face-to-face

conversation

telephone

call

email

chat

online

communication

on

Weblogs

and

so

on

If

we

wish

to

discover

the

social

relationship

by

observation

we

must

estimate

relationships

from

superficial

communication

The

emerging

field

of

social

network

mining

provides

methods

for

discovering

social

interactions

and

networks

from

legacy

sources

such

as

web

pages

databases

mailing

lists

and

personal

emails

Currently

we

use

three

kinds

of

information

sources

to

obtain

social

relationships

using

mining

techniques

From

the

Web

we

extract

social

networks

using

a

search

engine

and

the

co-occurrence

of

two

persons

names

on

the

Web

Consequently

we

can

determine

the

following

relationships

among

researchers

Coauthor

Same

affiliation

Same

project

Same

event

-LRB-

participants

of

the

same

conference

workshop

etc.

-RRB-

-LSB-

-RSB-

Coauthor

and

Same

event

correspond

to

an

event

co-participation

relationship

Same

affiliation

and

same

project

correspond

to

a

common

property

relationship

We

are

also

using

other

sources

such

as

email

and

sensors

-LRB-

we

are

developing

a

device

that

detects

users

within

social

spaces

such

as

parties

and

conferences

-RRB-

to

obtain

social

relationships

Necessarily

the

quality

of

information

obtained

by

mining

is

expected

to

be

inferior

to

that

of

manually

authored

profiles

We

can

reuse

those

data

if

a

user

has

already

declared

his

relationships

in

FOAF

or

profiles

of

social

networking

services

Although

users

might

find

it

difficult

and

demanding

to

record

social

relations

it

would

be

beneficial

to

ask

users

to

provide

information

to

obtain

social

relationships

In

addition

to

the

relationship

type

another

factor

of

the

social

relationship

is

tie

strength

Tie

strength

itself

is

a

complex

construct

of

several

characteristics

of

social

relations

It

is

definable

as

affective

frequency

trust

comple-mentarity

etc.

No

consensus

for

defining

and

measuring

them

exists

which

means

that

people

use

different

elicita-tion

methods

when

it

comes

to

determining

tie

strength

For

example

Orkut

a

huge

social

networking

service

allows

description

of

the

strength

of

friendship

relations

on

a

five-point

scale

from

have

n't

met

to

best

friend

whereas

other

sites

might

choose

other

scales

or

terms

In

our

system

we

use

trust

as

a

parameter

of

tie

strength

Trust

has

several

very

specific

definitions

In

-LSB-

-RSB-

Golbeck

describes

trust

as

credibility

or

reliability

in

a

human

sense

how

much

credence

should

I

give

to

what

this

person

speaks

about

and

based

on

what

my

friends

say

how

much

should

I

trust

this

new

person

In

the

context

of

information

sharing

trust

can

be

regarded

as

reliability

regarding

how

a

person

will

handle

my

information

Users

can

give

trust

directly

in

a

numerical

value

to

a

person

in

his

relation

Alternatively

trust

is

obtainable

automatically

as

authori-tativeness

of

each

person

using

the

social

network

-LSB-

-RSB-

The

obtained

social

network

data

are

integrated

as

extended

FOAF

files

and

stored

in

database

Users

can

adjust

networks

if

needed

-LRB-

Fig.

-RRB-

The

social

relationship

and

its

tie

strength

become

guiding

principles

when

a

user

determines

an

access

control

list

to

information

Social

Network

Analysis

for

Information

Sharing

The

system

enables

users

to

analyze

their

social

networks

to

provide

awareness

of

the

information

dissemination

process

within

the

social

network

Social

network

analysis

-LRB-

SNA

-RRB-

is

distinguishable

from

other

fields

of

sociology

by

its

focus

on

relationships

between

actors

rather

than

attributes

of

actors

a

network

view

and

a

belief

that

structure

affects

substantive

outcomes

Because

an

actor

's

position

in

a

network

affects

information

dissemination

SNA

provides

an

important

implication

for

information

sharing

on

the

social

network

For

example

occupying

a

favored

position

means

that

the

actor

will

have

better

access

to

information

resources

and

social

support

The

SNA

models

are

based

on

graphs

with

graph

measures

such

as

centrality

that

are

defined

using

a

sociological

interpretation

of

graph

structure

Freeman

proposes

numerous

ways

to

measure

centrality

-LSB-

-RSB-

Considering

a

social

network

of

actors

the

simplest

measure

is

to

count

the

number

of

others

with

whom

an

actor

maintains

relations

The

actor

with

the

most

connections

the

highest

degree

is

most

central

This

measure

is

called

degreeness

Another

measure

is

closeness

which

calculates

the

distance

from

each

actor

in

the

network

to

every

other

actor

based

on

connections

among

all

network

members

Central

actors

are

closer

to

all

others

than

are

other

actors

A

third

measure

is

betweenness

which

examines

the

extent

to

which

an

actor

is

situated

among

others

in

the

network

the

extent

to

which

Figure

Web

site

for

sharing

research

information

information

must

pass

through

them

to

get

to

others

and

consequently

the

extent

to

which

they

are

exposed

to

information

circulation

within

the

network

If

the

betweenness

of

an

actor

is

high

it

frequently

acts

as

a

local

bridge

that

connects

the

individual

to

other

actors

outside

a

group

In

terms

of

network

ties

this

kind

of

bridge

is

well

known

as

Granovetter

's

weak

tie

-LSB-

-RSB-

which

contrasts

with

strong

tie

within

a

densely-closed

group

As

the

weak

tie

becomes

a

bridge

between

different

groups

a

large

community

often

breaks

up

to

a

set

of

closely

knit

group

of

individuals

woven

together

more

loosely

according

to

occasional

interaction

among

groups

Based

on

this

theory

social

network

analysis

offers

a

number

of

clustering

algorithms

for

identifying

communities

based

on

network

data

The

system

provides

users

with

these

network

analyses

-LRB-

Fig.

-RRB-

so

that

they

can

decide

who

can

access

their

information

For

example

if

user

wants

to

diffuse

her

information

she

might

consider

granting

access

to

a

person

-LRB-

with

certain

trust

-RRB-

who

has

both

high

degreeness

and

betweenness

On

the

other

hand

she

must

be

aware

of

betweenness

when

the

information

is

private

or

confidential

Clustering

is

useful

when

a

user

wishes

to

share

information

within

a

certain

group

To

demonstrate

and

evaluate

our

system

we

developed

a

community

site

-LRB-

Fig.

-RRB-

using

communication

tools

such

as

Weblogs

Wikis

and

Forums

By

that

system

studies

from

different

organizations

and

projects

can

be

disseminated

and

their

information

thereby

shared

Users

can

share

various

information

such

as

papers

ideas

reports

and

schedules

at

the

site

Our

system

is

integrated

into

a

site

that

provides

access

control

to

that

information

Integrating

our

system

takes

advantage

of

the

open

and

information

nature

of

the

communication

tools

It

also

maintains

the

privacy

of

the

content

and

activities

of

those

applications

Users

can

manage

their

social

networks

-LRB-

Fig.

-RRB-

and

attach

the

access

control

list

to

their

content

-LRB-

e.g.

Blog

entries

profiles

and

Wiki

pages

-RRB-

using

extracted

social

relationships

and

social

network

analysis

-LRB-

Fig.

-RRB-

Once

a

user

determines

the

access

control

list

she

can

save

it

as

her

information

access

policy

for

corresponding

content

The

access

policy

is

described

using

extended

eXtensible

Access

Control

Markup

Language

-LRB-

XACML

-RRB-

and

is

stored

in

a

database

She

can

reuse

and

modify

the

previous

policy

if

she

subsequently

creates

a

similar

content

One

feature

of

our

system

is

that

it

is

easily

adaptable

to

new

applications

because

of

its

plug-and-play

design

We

are

planning

to

integrate

it

into

various

Web

sites

and

applications

such

as

social

network

sites

and

RSS

readers

Goecks

and

Mynatt

propose

a

Saori

infrastructure

that

also

uses

social

networks

for

information

sharing

-LSB-

-RSB-

They

obtain

social

networks

from

users

email

messages

and

provide

sharing

policies

based

on

the

type

of

information

We

obtain

social

networks

from

various

sources

and

integrate

them

into

FOAF

files

This

facilitates

the

importation

and

maintenance

of

social

network

data

Another

feature

is

that

our

system

enables

users

to

analyze

their

social

networks

Thereby

users

can

control

information

dissemination

more

effectively

and

flexibly

than

through

the

use

of

pre-defined

policies

As

users

increasingly

disseminate

their

information

on

the

Web

privacy

concerns

demand

that

access

to

particular

information

be

limited

We

propose

a

real-world

oriented

information

sharing

system

using

social

networks

It

enables

users

to

control

the

information

dissemination

process

within

social

networks

just

as

they

are

in

the

real

world

Future

studies

will

evaluate

the

system

with

regard

to

how

it

contributes

to

wider

and

safer

information

sharing

than

it

would

otherwise

We

will

also

develop

a

distributed

system

that

can

be

used

fully

on

the

current

Web

-LSB-

-RSB-

D.

Brickley

and

L.

Miller

FOAF

the

friend

of

a

friend

vocabulary

-LSB-

-RSB-

L.

C.

Freeman

Centrality

in

social

networks

Conceptual

clarification

Social

Networks

Vol

pp.

-LSB-

-RSB-

J.

Goecks

and

E.

D.

Mynatt

Leveraging

Social

Networks

for

Information

Sharing

In

Proc

of

CSCW

-LSB-

-RSB-

J.

Golbeck

J.

Hendler

and

B.

Parsia

Trust

networks

on

the

semantic

web

in

Proc

WWW

-LSB-

-RSB-

M.

Granovetter

Strength

of

weak

ties

American

Journal

of

Sociology

Vol

pp.

-LSB-

-RSB-

C.

Masolo

S.

Borgo

A.

Gangemi

N.

Guarinno

and

A.

Oltramari

WonderWeb

Deliverable

-LSB-

-RSB-

Y.

Matsuo

M.

Hamasaki

J.

Mori

H.

Takeda

and

K.

Hasida

Ontological

Consideration

on

Human

Relationship

Vocabulary

for

FOAF

In

Proc

of

the

Workshop

on

Friend

of

a

Friend

Social

Networking

and

Semantic

Web

-LSB-

-RSB-

Y.

Matsuo

H.

Tomobe

K.

Hasida

M.

Ishiz

uka

Finding

Social

Network

for

Trust

Calculation

In

Proc

of

European

Conference

on

Artificial

Intelligence

O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O


Modeling

Node

Compromise

Spread

in

Wireless

Sensor

Networks

Using

Epidemic

Theory

Motivated

by

recent

surfacing

viruses

that

can

spread

over

the

air

interfaces

in

this

paper

we

investigate

the

potential

disastrous

threat

of

node

compromise

spreading

in

wireless

sensor

networks

Originating

from

a

single

infected

node

we

assume

such

a

compromise

can

propagate

to

other

sensor

nodes

via

communication

and

preestablished

mutual

trust

We

focus

on

the

possible

epidemic

breakout

of

such

propagations

where

the

whole

network

may

fall

victim

to

the

attack

Based

on

epidemic

theory

we

model

and

analyze

this

spreading

process

and

identify

key

factors

determining

potential

outbreaks

In

particular

we

perform

our

study

on

random

graphs

precisely

constructed

according

to

the

parameters

of

the

network

such

as

distance

key

sharing

constrained

communication

and

node

recovery

thereby

reflecting

the

true

characteristics

therein

The

analytical

results

provide

deep

insights

in

designing

potential

defense

strategies

against

this

threat

Furthermore

through

extensive

simulations

we

validate

our

model

and

perform

investigations

on

the

system

dynamics

Index

Terms

Sensor

Networks

Epidemiology

Random

Key

Predistribution

Random

Graph

As

wireless

sensor

networks

are

unfolding

their

vast

potential

in

a

plethora

of

application

environments

-LSB-

-RSB-

security

still

remains

one

of

the

most

critical

challenges

yet

to

be

fully

addressed

In

particular

a

vital

problem

in

the

highly

distributed

and

resource

constrained

environment

is

node

compromise

where

a

sensor

node

can

be

completely

captured

and

manipulated

by

the

adversary

While

extensive

work

has

focused

on

designing

schemes

that

can

either

defend

and

delay

node

capture

or

timely

identify

and

revoke

compromised

nodes

themselves

-LSB-

-RSB-

little

attention

has

been

paid

to

the

node

compromise

process

itself

Inspired

by

recently

emerged

viruses

that

can

spread

over

air

interfaces

we

identify

in

this

paper

the

threat

of

epidemic

spreading

of

node

compromises

in

large

scale

wireless

sensor

networks

and

present

a

model

that

captures

the

unique

characteristic

of

wireless

sensor

networks

in

conjunction

with

pairwise

key

schemes

In

particular

we

identify

the

key

factors

determining

the

potential

epidemic

outbreaks

that

in

turn

can

be

employed

to

devise

corresponding

defense

strategies

A.

Motivation

Due

to

its

scarce

resources

and

hence

low

defense

capabilities

node

compromises

can

be

expected

to

be

common

phenomena

for

wireless

sensor

networks

in

unattended

and

hostile

environments

While

extensive

research

efforts

including

those

from

ourselves

-LSB-

-RSB-

have

been

engineered

toward

designing

resilient

network

security

mechanisms

-LSB-

-RSB-

-LSB-

-RSB-

the

compromise

itself

and

in

particular

the

propagation

of

node

compromise

-LRB-

possible

epidemics

-RRB-

have

attracted

little

attention

While

node

compromise

thanks

to

physical

capture

and

succeeding

analysis

is

naturally

constrained

by

the

adver-sary

's

capability

software

originated

compromises

can

be

much

more

damaging

Specifically

the

recently

surfaced

virus

Cabir

that

can

spread

over

the

air

interface

has

unveiled

a

disastrous

threat

for

wireless

sensor

networks

Inescapably

viruses

targeting

wireless

sensor

networks

will

emerge

Consequently

node

compromise

by

way

of

virus

spreading

-LRB-

over

the

air

interface

-RRB-

can

effortlessly

devastate

the

entire

network

in

a

short

period

of

time

With

recent

advancements

on

sensor

design

empowering

nodes

such

as

motes

with

over-the-air

programmability

the

network

becomes

vulnerable

to

the

above

described

attack

Even

worse

the

inherent

dense

large

scale

nature

of

sensor

networks

undoubtedly

further

facilitates

the

virus

propagation

While

virus

spreading

over

the

internet

has

been

widely

studied

and

notably

by

means

of

epidemic

theory

-LSB-

-RSB-

-LSB-

-RSB-

the

distance

and

pairwise

key

restricted

communication

pattern

in

wireless

sensor

networks

uniquely

distinguish

the

phenomena

from

those

on

the

Internet

B

Our

Contribution

In

this

paper

we

investigate

the

spreading

process

of

node

compromise

in

large

scale

wireless

sensor

networks

Proceedings

of

the

International

Symposium

on

a

World

of

Wireless

Mobile

and

Multimedia

Networks

-LRB-

WoWMoM

-RRB-

IEEE

Starting

from

a

single

point

of

failure

we

assume

that

the

adversary

can

effectively

compromise

neighboring

nodes

through

wireless

communication

and

thus

can

threat

the

whole

network

without

engaging

in

full

scale

physical

attacks

In

particular

due

to

security

schemes

employed

by

the

sensor

networks

we

assume

that

communication

can

only

be

performed

when

neighboring

nodes

can

establish

mutual

trust

by

authenticating

a

common

key

Therefore

node

compromise

is

not

only

determined

by

the

deployment

of

sensor

nodes

which

in

turn

affects

node

density

but

also

determined

by

the

pairwise

key

scheme

employed

therein

By

incorporating

these

factors

of

the

networks

we

propose

an

epidemiological

model

to

investigate

the

probability

of

a

breakout

-LRB-

compromise

of

the

whole

network

-RRB-

and

if

not

the

sizes

of

the

affected

components

-LRB-

compromised

clusters

of

nodes

-RRB-

Furthermore

we

analyze

the

effect

of

node

recovery

in

an

active

infection

scenario

and

obtain

critical

values

for

these

parameters

that

result

in

an

outbreak

Through

extensive

simulations

we

show

that

our

analytical

results

can

closely

capture

the

effects

in

a

wide

range

of

network

setups

The

remainder

of

the

paper

is

organized

as

follows

In

Section

II

we

present

the

preliminaries

including

the

threat

model

random

key

pre-distribution

and

epidemic

theory

In

Section

III

we

study

the

compromise

propagation

without

node

recovery

and

with

node

recovery

and

detail

our

analytical

results

We

perform

experimental

study

in

Section

IV

Related

work

is

presented

in

Section

V

and

we

conclude

in

Section

VI

In

this

section

we

present

our

threat

model

and

briefly

overview

pairwise

key

distribution

in

wireless

sensor

networks

and

epidemic

theory

A.

Threat

model

We

assume

that

a

compromised

node

by

directly

communicating

with

a

susceptible

node

can

spread

the

infection

and

conduce

to

the

compromise

of

the

susceptible

node

Communication

among

sensor

nodes

is

not

only

constrained

by

their

distances

but

also

shall

be

secured

and

thus

determined

by

the

probability

of

pairwise

key

sharing

Therefore

the

spreading

of

node

compromise

is

dependent

on

the

network

deployment

strategy

and

the

pairwise

key

scheme

employed

therein

We

assume

that

the

seed

compromise

node

could

be

originated

by

an

adversary

through

physical

capture

and

analysis

of

that

node

or

by

other

similar

means

The

spread

of

node

compromise

in

a

wireless

sensor

network

particularly

thanks

to

its

dense

nature

can

lead

to

an

epidemic

effect

where

the

whole

network

will

get

infected

We

consider

this

epidemic

effect

as

the

key

threat

to

the

network

and

hence

the

investigation

target

of

this

paper

B.

Pairwise

Key

Pre-distribution

As

the

pairwise

key

scheme

affects

the

communication

and

hence

the

propagation

of

the

node

compromise

we

provide

below

a

brief

overview

of

the

key

distribution

schemes

in

wireless

sensor

networks

Due

to

the

severe

resource

constraint

of

wireless

sensor

networks

and

limited

networking

bandwidth

proposed

pairwise

key

schemes

have

commonly

adopted

the

predistribution

approach

instead

of

online

key

management

schemes

with

prohibitive

resource

consumption

The

concept

of

pre-distribution

was

originated

from

-LSB-

-RSB-

where

the

authors

propose

to

assign

a

number

of

keys

termed

key

ring

randomly

drawn

from

a

key

pool

If

two

neighboring

nodes

share

a

common

key

on

their

key

rings

a

shared

pairwise

key

exists

and

a

secure

communication

can

be

established

Pre-distribution

schemes

that

rely

on

bivariate

polynomials

is

discussed

in

-LSB-

-RSB-

In

this

scheme

each

sensor

node

is

pre-distributed

a

set

of

polynomials

Two

sensor

nodes

with

the

same

polynomials

can

respectively

derive

the

same

key

Regardless

of

the

specific

key

distribution

scheme

a

common

parameter

capturing

the

performance

is

the

probability

that

two

neighbors

can

directly

establish

a

secure

communication

We

denote

this

probability

by

q

As

it

shall

be

revealed

later

q

plays

an

important

role

in

the

spreading

of

node

compromise

because

direct

communication

as

explained

in

the

threat

model

can

result

in

propagation

of

malicious

code

C.

Node

Recovery

In

the

event

that

a

node

is

compromised

its

secrets

will

be

revealed

to

the

attacker

The

network

may

attempt

to

recover

the

particular

node

Recovery

might

be

realized

in

several

possible

ways

For

example

the

keys

of

the

nodes

might

be

revoked

and

the

node

may

be

given

a

fresh

set

of

secret

keys

In

this

context

key

revocation

which

refers

to

the

task

of

securely

removing

keys

that

are

known

to

be

compromised

has

been

investigated

as

part

of

the

key

management

schemes

for

example

in

-LSB-

-RSB-

Moreover

recovery

can

also

be

achieved

by

simply

removing

the

compromised

node

from

the

network

for

example

by

announcing

a

blacklist

or

simply

reload

the

node

's

programs

More

sophisticated

methods

may

include

immunizing

a

node

with

an

appropriate

antivirus

patch

that

might

render

the

node

immune

from

the

same

virus

attack

Regardless

in

our

analysis

we

will

study

virus

spreading

under

the

two

cases

respectively

depending

on

whether

a

node

can

be

recovered

or

not

D.

Epidemic

Theory

Originally

epidemic

theory

concerns

about

contagious

diseases

spreading

in

the

human

society

The

key

feature

of

epidemiology

-LSB-

-RSB-

-LSB-

-RSB-

is

the

measurement

of

infection

outcomes

in

relation

to

a

population

at

risk

The

population

at

risk

basically

comprises

of

the

set

of

people

who

possess

a

susceptibility

factor

with

respect

to

the

infection

This

factor

is

dependent

on

several

parameters

including

exposure

spreading

rate

previous

frequency

of

occurrence

etc.

which

define

the

potential

of

the

disease

causing

the

infection

Example

models

characterizing

the

infection

spreading

process

include

the

Susceptible

Infected

Susceptible

-LRB-

SIS

-RRB-

Model

Susceptible

Infected

Recovered

-LRB-

SIR

-RRB-

Model

etc.

In

the

former

a

susceptible

individual

acquires

infection

and

then

after

an

infectious

period

-LRB-

i.e.

the

time

Proceedings

of

the

International

Symposium

on

a

World

of

Wireless

Mobile

and

Multimedia

Networks

-LRB-

WoWMoM

-RRB-

IEEE

the

infection

persists

-RRB-

the

individual

becomes

susceptible

again

On

the

other

hand

in

the

latter

the

individual

recovers

and

becomes

immune

to

further

infections

Of

particular

interest

is

the

phase

transition

of

the

spreading

process

that

is

dependent

on

an

epidemic

threshold

if

the

epidemic

parameter

is

above

the

threshold

the

infection

will

spread

out

and

become

persistent

on

the

contrary

if

the

parameter

is

below

the

threshold

the

virus

will

die

out

Epidemic

theory

indeed

has

been

borrowed

to

the

networking

field

to

investigate

virus

spreading

In

this

paper

we

will

mainly

rely

on

a

random

graph

model

to

characterize

the

unique

connectivity

of

the

sensor

network

and

perform

the

epidemic

study

-LSB-

-RSB-

-LSB-

-RSB-

In

this

section

we

analyze

the

propagation

of

node

compromise

originating

from

a

single

node

that

has

been

affected

Our

focus

is

to

study

the

outbreak

point

of

the

epidemic

effect

where

the

whole

network

will

fall

victim

to

the

compromise

procedure

Our

key

method

is

to

characterize

the

sensor

network

including

its

key

distribution

by

mathematically

formulating

it

as

a

random

graph

whose

key

parameters

are

precisely

determined

by

those

of

the

sensor

network

Therefore

the

investigation

of

epidemic

phenomena

can

be

performed

on

the

random

graph

instead

Following

this

approach

we

observe

the

epidemic

process

under

two

scenarios

without

node

recovery

and

with

node

recovery

depending

on

whether

infected

nodes

will

be

recovered

by

external

measures

like

key

revocation

immunization

etc.

A.

Network

Model

as

Random

Graph

Assume

that

sensor

nodes

are

uniformly

deployed

in

a

disc

area

with

radius

R.

Let

N

R

denote

the

node

density

of

the

network

where

N

is

the

total

number

of

the

nodes

For

a

sensor

node

with

communication

range

r

the

probability

that

l

nodes

are

within

its

communication

range

is

given

by

p

-LRB-

l

-RRB-

nl

p

l

-LRB-

p

-RRB-

n-l

-LRB-

-RRB-

where

p

is

defined

by

p

r

R

r

N

-LRB-

-RRB-

Thus

p

is

the

probability

of

a

link

existing

at

the

physical

level

i.e.

whether

the

two

nodes

fall

within

their

respective

communication

ranges

We

further

assume

that

the

probability

that

two

neighboring

nodes

sharing

at

least

one

key

in

the

random

predistribution

pairwise

key

is

q.

Notice

that

q

is

determined

by

the

specific

pairwise

key

scheme

employed

For

a

particular

node

having

l

neighboring

nodes

the

probability

that

there

are

k

nodes

k

l

sharing

at

least

one

key

with

it

is

given

by

p

-LRB-

k

l

-RRB-

lk

q

k

-LRB-

q

-RRB-

l-k

-LRB-

-RRB-

Therefore

the

probability

of

having

k

neighboring

nodes

sharing

at

least

one

key

is

p

-LRB-

k

-RRB-

l

k

p

-LRB-

l

-RRB-

p

-LRB-

k

l

-RRB-

-LRB-

-RRB-

l

k

n

l

p

l

-LRB-

p

-RRB-

n-l

l

k

q

k

-LRB-

q

-RRB-

l-k

-LRB-

-RRB-

Thus

based

on

both

physical

proximity

and

the

probability

of

key

sharing

between

neighbors

we

get

a

degree

distribution

p

-LRB-

k

-RRB-

Notice

that

this

degree

distribution

can

be

employed

to

generate

a

random

graph

G

Since

G

possesses

the

same

property

in

terms

of

secure

communication

pattern

as

the

sensor

network

of

concern

we

will

next

perform

the

analysis

on

G

instead

B.

Compromise

Spread

Without

Node

Recovery

Given

the

random

graph

construction

we

now

analyze

the

case

of

compromise

spread

when

no

node

recovery

is

performed

In

other

words

a

compromised

sensor

node

will

remain

infectious

indefinitely

Let

G

-LRB-

x

-RRB-

be

the

generating

function

of

the

degree

distribution

of

a

randomly

chosen

vertex

in

G

and

is

defined

by

G

-LRB-

x

-RRB-

k

p

-LRB-

k

-RRB-

x

k

-LRB-

-RRB-

Moreover

with

G

-LRB-

x

-RRB-

given

by

G

-LRB-

x

-RRB-

G

-LRB-

-RRB-

G

-LRB-

x

-RRB-

-LRB-

-RRB-

and

with

denoting

the

infection

probability

of

a

node

being

infected

by

communicating

with

a

compromised

node

then

following

the

analysis

presented

in

-LSB-

-RSB-

the

average

size

of

the

outbreak

is

derived

as

s

G

-LRB-

-RRB-

G

-LRB-

-RRB-

-LRB-

-RRB-

Infection

probability

essentially

captures

the

spreading

capability

of

the

virus

that

could

compromise

the

network

the

larger

it

is

the

stronger

the

virus

is

We

assume

that

its

value

can

be

obtained

by

means

of

measurement

or

analysis

Given

the

above

result

we

can

see

that

the

outbreak

point

for

the

network

is

-LRB-

-RRB-

which

marks

the

onset

of

an

epidemic

For

-LRB-

-RRB-

we

have

an

epidemic

in

the

form

of

a

giant

component

in

the

random

network

and

the

size

S

of

the

epidemic

where

S

denotes

the

expected

fraction

of

the

network

that

will

be

compromised

if

an

outbreak

happens

is

given

by

S

G

-LRB-

u

-RRB-

Here

u

is

the

root

of

the

self-consistency

relation

u

G

-LRB-

u

-RRB-

Intuitively

the

above

conclusion

reveals

that

if

-LRB-

-RRB-

the

component

of

compromised

nodes

is

finite

Proceedings

of

the

International

Symposium

on

a

World

of

Wireless

Mobile

and

Multimedia

Networks

-LRB-

WoWMoM

-RRB-

IEEE

Infection

Probability

-LRB-

-RRB-

Size

of

Cluster

Compromised

q

q

q

q

q

Key

Sharing

Prob

-LRB-

a

-RRB-

Non-epidemic

cluster

size

vs.

infection

probability

-LRB-

-RRB-

Infection

Probability

-LRB-

-RRB-

Fraction

of

Network

Compromised

q

q

q

q

q

Key

Sharing

Prob

-LRB-

b

-RRB-

Epidemic

size

vs.

infection

probability

-LRB-

-RRB-

Fig.

Size

of

compromised

node

clusters

-LRB-

a

-RRB-

depicts

the

average

size

of

infected

clusters

when

there

is

no

epidemic

and

-LRB-

b

-RRB-

shows

the

epidemic

size

as

the

fraction

of

the

entire

network

The

point

where

non-zero

value

appears

indicates

the

transition

from

non-epidemic

to

epidemic

in

size

regardless

of

the

size

of

the

network

and

each

node

's

probability

of

being

compromised

is

zero

for

large

networks

On

the

contrary

if

-LRB-

-RRB-

there

always

exists

a

finite

probability

for

a

node

to

be

compromised

Fig.

depicts

this

effect

for

a

network

with

N

nodes

with

different

key

sharing

probabilities

q

The

underlying

physical

topology

determined

by

the

communication

range

and

node

density

has

an

average

edge

probability

of

p

Given

the

physical

deployment

we

vary

the

probability

of

direct

pairwise

key

sharing

-LRB-

q

-RRB-

and

study

the

point

of

outbreak

As

we

can

see

in

Fig.

while

undoubtedly

increasing

q

can

facilitate

communication

in

the

network

the

network

also

becomes

more

vulnerable

to

virus

spreading

Specifically

when

q

network

wide

breakout

is

only

possible

when

a

compromised

node

has

an

infection

probability

-LRB-

-RRB-

larger

than

to

infect

a

neighbor

We

note

that

in

this

case

we

have

an

average

node

degree

of

On

the

contrary

this

probability

only

needs

to

be

around

when

q

which

subsequently

makes

the

node

degree

Fig.

-LRB-

b

-RRB-

illustrates

the

fraction

of

the

network

that

is

ultimately

infected

as

the

infection

probability

is

increased

beyond

the

critical

point

of

the

onset

of

outbreak

For

instance

we

observe

that

when

q

the

whole

network

is

compromised

with

a

value

of

less

than

On

the

contrary

with

q

of

the

network

could

be

compromised

with

only

a

high

value

of

In

summary

Fig.

clearly

indicates

the

tradeoff

between

key

sharing

probability

among

sensor

nodes

and

the

vulnerability

of

the

network

to

compromise

C.

Compromise

Spread

With

Node

Recovery

In

this

case

we

assume

that

the

network

has

the

capability

to

recover

some

of

the

compromised

nodes

by

either

immunization

or

removal

from

the

network

To

capture

this

recovery

effect

we

assume

that

an

infected

node

recovers

or

is

removed

from

the

network

after

an

average

duration

of

infectivity

In

other

words

a

node

in

the

sensor

network

remains

infective

for

an

average

period

after

which

it

is

immunized

During

this

infective

period

the

node

transmits

the

epidemic

to

its

neighbors

with

the

infection

rate

denoting

the

probability

of

infection

per

unit

time

Evidently

the

parameter

is

critical

to

the

analysis

as

it

measures

how

soon

a

compromised

node

recovers

Naturally

we

will

perform

our

analysis

following

the

SIR

model

in

epidemic

theory

-LSB-

-RSB-

-LSB-

-RSB-

First

consider

a

pair

of

adjacent

nodes

where

one

is

infected

and

the

other

is

susceptible

If

T

denotes

the

compromise

transmission

probability

given

the

above

definitions

for

and

we

can

say

that

the

probability

that

the

disease

will

not

be

transmitted

from

the

infected

to

the

susceptible

is

given

by

T

lim

-LRB-

t

-RRB-

t

e

-LRB-

-RRB-

Subsequently

we

have

the

transmission

probability

T

e

In

other

words

the

compromise

propagation

can

be

considered

as

a

Poisson

process

with

average

The

outcome

of

this

process

is

the

same

as

bond

percolation

and

T

is

basically

analogous

to

the

bond

occupation

probability

on

the

graph

representing

the

key

sharing

network

Thus

the

outbreak

size

would

be

precisely

the

size

of

the

cluster

of

vertices

that

can

be

reached

from

the

initial

vertex

-LRB-

infected

node

-RRB-

by

traversing

only

occupied

edges

which

are

occupied

with

probability

T

Notice

that

T

explicitly

captures

node

recovery

in

terms

of

the

parameter

Replacing

with

T

in

Equation

and

following

similar

steps

we

get

the

size

of

the

average

cluster

as

s

T

G

-LRB-

-RRB-

T

G

-LRB-

-RRB-

-LRB-

-RRB-

and

the

epidemic

size

is

obtained

by

S

G

-LRB-

u

T

-RRB-

-LRB-

-RRB-

where

u

is

obtained

by

u

G

-LRB-

u

T

-RRB-

-LRB-

-RRB-

Proceedings

of

the

International

Symposium

on

a

World

of

Wireless

Mobile

and

Multimedia

Networks

-LRB-

WoWMoM

-RRB-

IEEE

Infectivity

Duration

Size

of

Cluster

Compromised

Infection

rate

-LRB-

a

-RRB-

Non-epidemic

cluster

size

vs.

infectivity

duration

Infectivity

Duration

Fraction

of

Network

Compromised

infection

rate

-LRB-

b

-RRB-

Epidemic

size

vs.

infectivity

duration

Fig.

Size

of

compromised

node

clusters

-LRB-

a

-RRB-

depicts

the

average

size

of

infected

clusters

when

there

is

no

epidemic

and

-LRB-

b

-RRB-

shows

the

epidemic

size

as

the

fraction

of

the

entire

network

The

point

where

non-zero

value

appears

indicates

the

transition

from

non-epidemic

to

epidemic

and

G

-LRB-

u

T

-RRB-

and

G

-LRB-

u

T

-RRB-

are

given

respectively

by

G

-LRB-

u

T

-RRB-

G

-LRB-

-LRB-

u

-RRB-

T

-RRB-

-LRB-

-RRB-

and

G

-LRB-

u

T

-RRB-

G

-LRB-

-LRB-

u

-RRB-

T

-RRB-

-LRB-

-RRB-

Fig.

summarizes

this

effect

depicting

the

epidemic

outbreak

against

the

average

recovery

time

for

the

respective

infection

rates

The

plots

are

for

a

sensor

network

with

typical

average

degree

of

In

Fig.

-LRB-

a

-RRB-

we

can

identify

the

average

duration

that

an

infected

node

is

allowed

to

remain

infective

before

an

epidemic

outbreak

occurs

We

notice

that

when

the

infection

rate

is

infected

nodes

have

to

be

recovered/removed

on

the

average

in

less

than

time

units

in

order

to

prevent

an

epidemic

As

expected

this

time

is

much

lower

when

the

infection

rate

is

Fig.

-LRB-

b

-RRB-

depicts

the

epidemic

outbreak

point

for

different

infection

rates

in

terms

of

the

average

duration

of

infectivity

of

a

node

We

remark

that

both

the

analytical

and

experimental

results

have

significant

implication

for

security

scheme

design

in

terms

of

revoking/immunizing

compromised

nodes

in

wireless

sensor

networks

it

dictates

the

speed

at

which

the

network

must

react

in

order

to

contain/prevent

the

effect

of

network

wide

epidemic

We

employ

a

discrete

event-driven

simulation

to

accu-rately

simulate

the

propagation

of

the

infection

spreading

process

In

this

section

we

first

outline

our

discrete-event

driven

simulation

model

for

the

gradual

progress

of

the

spread

of

node

compromise

Then

we

use

this

model

to

capture

the

time

dynamics

of

the

spread

of

the

compromise

in

the

whole

population

A.

Simulation

Setup

In

our

simulation

we

assume

the

number

of

sensor

nodes

in

the

network

to

be

The

sensor

network

is

produced

by

uniformly

distributing

the

sensors

in

a

unit

area

The

communication

range

of

each

node

is

assumed

to

be

units

Our

goal

is

to

make

the

physical

network

fairly

connected

with

an

average

node

degree

of

around

to

We

use

the

key

sharing

probability

on

top

of

this

network

to

further

reduce

the

average

node

degree

of

the

final

key

sharing

network

to

typical

values

of

and

We

employ

the

random

key

pre-distribution

scheme

described

in

-LSB-

-RSB-

to

establish

the

pairwise

key

among

sensor

nodes

By

tuning

the

parameters

of

the

scheme

we

can

achieve

any

specific

values

for

the

probability

of

any

two

neighbors

to

share

at

least

one

key

Our

simulation

works

in

two

phases

In

the

first

phase

we

form

the

network

where

each

node

identifies

its

set

of

neighbors

and

entries

are

made

into

a

neighbor

table

The

average

degree

of

the

key

sharing

network

is

controlled

by

changing

the

value

of

the

key

sharing

probability

between

neighbors

The

entry

for

each

node

in

the

neighborhood

table

can

indicate

whether

a

node

is

susceptible

infected

or

recovered

We

use

typical

values

obtained

for

the

average

node

degree

of

the

network

namely

and

In

the

second

phase

we

simulate

actual

virus

propagation

Initially

at

t

the

number

of

infected

nodes

denoted

by

I

-LRB-

-RRB-

is

set

to

be

At

any

time

point

t

the

population

is

divided

into

the

group

of

susceptible

nodes

S

-LRB-

t

-RRB-

and

the

group

of

infected

nodes

I

-LRB-

t

-RRB-

In

the

situation

where

we

have

nodes

that

are

immunized

and

thus

recovered

we

denote

that

this

set

of

recovered

nodes

by

R

-LRB-

t

-RRB-

The

sub-population

dynamics

is

obtained

by

observing

the

population

counts

after

fixed

simulation

intervals

of

time

unit

We

assume

that

the

time

it

takes

for

an

infected

node

to

infect

its

susceptible

neighbor

is

negative

exponentially

distributed

with

a

mean

of

unit

time

There

are

two

simulation

scenarios

corresponding

to

our

analysis

B.

Simulation

Results

and

Discussion

-RRB-

Simulation

Results

for

No

Recovery

Case

The

simulation

results

for

the

case

without

recovery

are

shown

Proceedings

of

the

International

Symposium

on

a

World

of

Wireless

Mobile

and

Multimedia

Networks

-LRB-

WoWMoM

-RRB-

IEEE

Time

-LRB-

t

-RRB-

Fraction

of

compromised

nodes

Growth

of

infected

nodes

with

time

N

z

-LRB-

a

-RRB-

Average

node

degree

Time

-LRB-

t

-RRB-

Fraction

of

compromised

nodes

Growth

of

infected

nodes

with

time

N

Avg

Degree

z

Infection

Prob

-LRB-

b

-RRB-

Average

node

degree

Fig.

System

dynamics

without

recovery

in

Fig.

We

vary

the

value

of

the

infection

probability

under

different

network

connectivities

and

study

the

time

dynamics

of

the

infected

population

We

notice

as

expected

that

an

increase

in

the

average

node

degree

from

to

has

an

impact

on

the

rate

of

compromise

of

the

network

For

instance

the

curve

with

the

lowest

value

-LRB-

-RRB-

has

compromised

the

entire

network

by

simulation

time

when

the

average

node

degree

is

However

with

the

node

degree

at

a

value

of

could

compromise

upto

of

the

network

by

that

same

simulation

time

Thus

we

find

that

in

the

no-recovery

case

the

two

key

parameters

affecting

the

network

compromise

rate

are

infection

probability

and

the

average

node

degree

-RRB-

Simulation

Results

for

Recovery

Case

Fig.

and

show

the

simulation

results

for

the

three

sub-populations

-LRB-

infected

immunized

and

susceptible

-RRB-

in

the

situation

where

nodes

do

recover

In

Fig.

we

see

the

effects

of

the

infectivity

duration

and

infection

rate

on

the

dynamics

of

the

epidemic

In

Fig.

-LRB-

c

-RRB-

the

highest

point

is

reached

very

fast

because

of

the

high

value

of

Thereafter

its

recovery

also

takes

less

time

However

in

Fig.

-LRB-

a

-RRB-

is

smaller

but

is

higher

-LRB-

i.e.

-RRB-

the

infection

rises

slowly

and

also

falls

slowly

because

of

the

high

recovery

time

In

comparison

Fig.

has

better

connectivity

of

average

node

degree

of

which

in

turn

increases

the

rate

of

infection

significantly

Comparing

Fig.

-LRB-

c

-RRB-

and

Fig.

-LRB-

c

-RRB-

we

observe

that

infection

penetration

is

higher

in

the

latter

even

in

presence

of

a

smaller

value

of

In

Fig.

-LRB-

c

-RRB-

it

shows

that

even

with

a

low

value

of

the

infection

still

rises

to

above

Therefore

we

observe

that

network

connectivity

has

a

high

impact

on

the

infection

propagation

and

on

the

speed

of

reaching

the

maximal

point

of

outbreak

However

thereafter

during

the

recover

phase

affects

aggressively

the

time

it

takes

to

recover

the

whole

network

The

mathematical

modeling

of

epidemics

is

well

documented

-LSB-

-RSB-

-LSB-

-RSB-

In

fact

visualizing

the

population

as

a

complex

network

of

interacting

individuals

has

resulted

in

the

analysis

of

epidemics

from

a

network

or

graph

theoretic

point

of

view

-LSB-

-RSB-

-LSB-

-RSB-

-LSB-

-RSB-

Node

compromise

in

sensor

networks

and

the

need

for

their

security

has

also

received

immense

attention

-LSB-

-RSB-

A

large

portion

of

current

research

on

security

in

sensor

networks

has

been

focused

on

protocols

and

schemes

for

securing

the

communication

between

nodes

-LSB-

-RSB-

-LSB-

-RSB-

Revocation

of

keys

of

compromised

nodes

has

been

studied

in

-LSB-

-RSB-

In

-LSB-

-RSB-

the

authors

demonstrate

the

ease

with

which

a

sensor

node

can

be

compromised

and

all

its

information

extracted

Unfortunately

little

work

has

been

done

on

the

defense

strategies

when

the

compromise

of

a

single

node

could

be

used

to

compromise

other

nodes

over

the

air

In

this

paper

we

take

the

first

step

to

model

this

potential

disastrous

propagation

In

-LSB-

-RSB-

the

authors

used

an

epidemic

modeling

technique

for

information

dissemination

in

a

MANET

However

they

assumed

homogeneous

mixing

which

is

not

possible

in

a

static

sensor

network

as

ours

In

our

work

we

adopted

some

of

the

results

presented

in

-LSB-

-RSB-

where

the

author

proposes

a

percolation

theory

based

evaluation

of

the

spread

of

an

epidemic

on

graphs

with

given

degree

distributions

However

little

has

been

shown

there

on

the

temporal

dynamics

of

the

epidemic

spread

and

the

authors

only

studied

the

final

outcome

of

an

infection

spread

In

this

paper

we

investigate

the

potential

threat

for

compromise

propagation

in

wireless

sensor

networks

Based

on

epidemic

theory

we

model

the

process

of

compromise

spreading

from

a

single

node

to

the

whole

network

In

particular

we

focus

on

the

key

network

parameters

that

determine

a

potential

epidemic

outbreak

in

the

network

Due

to

the

unique

distance

and

key

sharing

constrained

communication

pattern

we

resort

to

a

random

graph

model

which

is

precisely

generated

according

to

the

parameters

of

the

real

sensor

network

and

perform

the

study

on

the

graph

Furthermore

we

introduce

the

effect

of

node

recovery

after

compromise

and

adapt

our

model

to

accommodate

this

effect

Our

results

reveal

key

network

parameters

in

defending

and

containing

potential

epidemics

In

particular

Proceedings

of

the

International

Symposium

on

a

World

of

Wireless

Mobile

and

Multimedia

Networks

-LRB-

WoWMoM

-RRB-

IEEE

Time

-LRB-

t

-RRB-

Fraction

of

Network

Dynamics

of

the

Infected

Susceptible

and

Revoked

nodes

Susceptible

S

-LRB-

t

-RRB-

Infected

I

-LRB-

t

-RRB-

Recovered

R

-LRB-

t

-RRB-

Average

Degree

z

Infection

Prob

-LRB-

a

-RRB-

Time

-LRB-

t

-RRB-

Fraction

of

Network

Dynamics

of

the

Infected

Susceptible

and

Revoked

nodes

S

-LRB-

t

-RRB-

I

-LRB-

t

-RRB-

R

-LRB-

t

-RRB-

z

-LRB-

b

-RRB-

Time

-LRB-

t

-RRB-

Fraction

of

Network

Dynamics

of

the

Infected

Susceptible

and

Revoked

nodes

S

-LRB-

t

-RRB-

I

-LRB-

t

-RRB-

R

-LRB-

t

-RRB-

z

-LRB-

c

-RRB-

Fig.

The

dynamics

of

the

population

with

recovery

for

average

degree

of

Time

-LRB-

t

-RRB-

Fraction

of

Network

Dynamics

of

the

Infected

Susceptible

and

Revoked

nodes

Susceptible

S

-LRB-

t

-RRB-

Infected

I

-LRB-

t

-RRB-

Recovered

R

-LRB-

t

-RRB-

Average

Degree

z

Infection

Prob

-LRB-

a

-RRB-

Time

-LRB-

t

-RRB-

Fraction

of

Network

Dynamics

of

the

Infected

Susceptible

and

Revoked

nodes

S

-LRB-

t

-RRB-

I

-LRB-

t

-RRB-

R

-LRB-

t

-RRB-

z

-LRB-

b

-RRB-

Time

-LRB-

t

-RRB-

Fraction

of

Network

Dynamics

of

the

Infected

Susceptible

and

Revoked

nodes

S

-LRB-

t

-RRB-

I

-LRB-

t

-RRB-

R

-LRB-

t

-RRB-

z

-LRB-

c

-RRB-

Fig.

The

dynamics

of

the

population

with

recovery

for

average

degree

of

the

result

provides

benchmark

time

period

for

the

network

to

recover

a

node

in

order

to

defend

against

the

epidemic

spreading

Our

extensive

simulation

results

validate

our

analyses

and

moreover

provide

insights

of

the

dynamics

of

the

system

in

terms

of

temporal

evolution

-LSB-

-RSB-

I

Akyildiz

W.

Su

Y

Sankarasubramaniam

and

E.

Cayirci

A

Survey

on

sensor

networks

IEEE

Communications

Magazine

vol

no.

-LSB-

-RSB-

R.

M.

Anderson

and

R.

M.

May

Infectious

Diseases

of

Human

Dynamics

and

Control

-LRB-

Oxford

Univ.

Press

Oxford

-RRB-

-LSB-

-RSB-

S.

Staniford

V.

Paxson

and

N.

Weaver

How

to

Own

the

Internet

in

Your

Spare

Time

In

Usenix

Security

Symposium

San

Francisco

August

-LSB-

-RSB-

C.

Hartung

J.

Balasalle

and

R.

Han

Node

Compromise

in

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Networks

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Need

for

Secure

Systems

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Report

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-LRB-

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Chan

V.

D.

Gligor

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G.

Muralidharan

On

the

Distribution

and

Revocation

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Cryptographic

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in

Sensor

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IEEE

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on

Dependable

and

Secure

Computing

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A.

Khelil

C.

Becker

J.

Tian

K.

Rothermel

An

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for

Information

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in

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MSWiM

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J.

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its

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Newman

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of

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Phys

Rev.

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art

no.

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C.

Moore

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M.

E.

J.

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and

percolation

in

small

world

networks

Phys

Rev.

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-RRB-

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P.

Grassberger

On

the

critical

behavior

of

the

general

epidemic

process

and

dynamic

percolation

Math

Biosc

-LRB-

-RRB-

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L

Eschenauer

and

V.

D.

Gligor

A

key-management

scheme

for

distributed

sensor

networks

in

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of

the

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CCS

pages

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D.C.

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H.

Chan

A.

Perrig

and

D.

Song

Random

key

predistribution

schemes

for

sensor

networks

in

Proc

of

the

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on

Research

in

Security

and

Privacy

SP

pages

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D.C.

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-RSB-

Donggang

Liu

and

Peng

Ning

Establishing

pairwise

keys

in

distributed

sensor

networks

in

Proc

of

the

ACM

Conference

on

Computer

and

Communications

Security

CCS

pages

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D.C.

USA

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-LSB-

-RSB-

H.

Chan

V.D.

Gligor

A.

Perrig

G.

Muralidharan

On

the

Distribution

and

Revocation

of

Cryptographic

Keys

in

Sensor

Networks

IEEE

Transactions

on

Dependable

and

Secure

Computing

Volume

Issue

July-Sept

-LSB-

-RSB-

A.

Chadha

Y.

Liu

and

S.

Das

Group

key

distribution

via

local

collaboration

in

wireless

sensor

networks

in

Proceedings

of

the

IEEE

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Clara

CA

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Proceedings

of

the

International

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on

a

World

of

Wireless

Mobile

and

Multimedia

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-LRB-

WoWMoM

-RRB-

IEEE

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