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8. Trust in P2P Systems

Prof. Bharat BhargavaCenter for Education and Research in Information Assurance and Security

(CERIAS)and

Department of Computer SciencesPurdue University

http://www.cs.purdue.edu/people/bb bb@cs.purdue.edu

Collaborators in the RAID Lab (http://raidlab.cs.purdue.edu):Mr. Ahmet Burak Can (Ph.D. Student)

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Trust in P2P Systems

Outline1) Introduction

1.1) Mitigating Attacks in P2P Systems1.2) Assumptions for Peer Interactions

2) Contexts of Trust in P2P Systems3) Definitions for the Proposed Solution4) Trust Metrics5) Trust-based Decisions 6) Interaction Evaluation by Peers7) Recommendation Evaluation by Peers8) Simulation Experiments

8.1) Attacker Models for Simulation:Individual attackers/ Collaborators / Pseudospoofers

8.2) Experimental Results

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1) Introduction1.1) Mitigating Attacks in P2P Systems

Mitigating attacks in a malicious P2P environment Use trust relationships among peers to mitigate

attacks in a malicious P2P environment

Algorithms are needed to establish trust among peers

Research tasks: Propose trust metrics that reflect all aspects of trust. Develop distributed algorithms to manage trust

relationships among peers and help them to make decisions using trust metrics

Define methods to evaluate interactions and trust information exchanged among peers (recommendations)

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1.2) Assumptions for Peer Interactions

Peers use no a priori information to establish trust No pre-existing trust relationships among peers

A peer must contribute and behave well to gain and preserve trust of another peer Malicious behavior of Peer 1 against Peer 2 can

easily destroy trust of Peer 2 in Peer 1

Trust metrics should have sufficient precision Required to rank peers accurately (according their

trustworthiness)

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2) Contexts of Trust in P2P Systems

Two contexts of trust — w.r.t. performing 2 different tasks:

1) Providing services to other peers 2) Giving recommendations to other peers.

These contexts considered separately A peer might simultaneously be a good

service provider and a bad recommender (or vice versa)

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3) Definitions for the Proposed Solution

A peer becomes an acquaintance of another peer after providing it a service (e.g., uploading a file)

Using a service from a peer is called a service interaction

All peers are strangers to each other at the start A peer expands its set of acquaintances by using services

from strangers

A recommendation represents the acquaintance’s trust information about a stranger

A peer requests recommendations about a stranger only from its acquaintances

Receiving a recommendation from an acquaintance is a recommendation interaction

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4) Trust Metrics (1)

Reputation is the primary metric when deciding about strangers in the service context Recommendations from acquaintances used

to calculate reputation metric

Service trust is a metric to measure trustworthiness of a peer in the service context A service provider is selected according to

service trust and reputation metric Service trust metric of a peer calculated

based on its past service interactions and its reputation

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4) Trust Metrics (2)

Recommendation trust is the primary metric to measure trustworthiness of a peer in the recommendation context I.e., when selecting recommenders and

evaluating recommendations Recommendation trust metric of a peer

calculated based on past recommendation interactions and its reputation

Analogously to service trust metric

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5) Trust-based Decisions (1)

When making trust decisions, interactions and reputation are considered separately

This helps when making a distinction between two trustworthy peers

Trust decisions about a stranger are based on reputation

Trust decisions about an acquaintance are based on its past interactions and reputation

As more interactions happen with an acquaintance, the experience derived through interactions becomes more important than its reputation

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5) Trust-based Decisions (2)

Using available acquaintances by a peer If no acquaintances - simply trust any stranger

providing the requested service

If some acquaintances - calculate reputation of strangers based on recommendations of acquaintances

May select one of the strangers May choose not to entrust strangers if

acquaintances can deliver the needed service

As more acquaintances become available – can become more selective

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6) Interaction Evaluation by Peers

Using all available information about interactions is helpful to calculate trust metrics more precisely A peer should be able to express its level of

satisfaction about an interaction Considering several parameters

E.g., online/offline periods, bandwidth, delay of the uploader in a file download operation

Service interactions might have varying importance

E.g., downloading a large file more important than downloading a small file

The effect of an interaction on trust calculation fades as new interactions occur

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7) Recommendation Evaluation by Peers

A recommendation makes a clear distinction between the recommender’s own experience and second-hand information collected from its acquaintances

This distinction enables more precise calculation of reputation

A recommendation contains the recommender’s level of confidence in the information provided

If the recommender has a low confidence, the recommendation is weak

A weak recommendation’s effect on the calculated reputation value is less than a strong one

A recommending peer is no more liable than its confidence in its recommendation

A recommendation from Peer 2 (the recommender) is evaluated by Peer 1 based on the value of recommendation trust metric that Peer 1 has for Peer 2

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8) Simulation Experiments

A file sharing application was simulated To understand the proposed algorithms for mitigating

attacks related to services and recommendations

The results of several empirical studies are used to simulate peer, resource, and network parametersSome of the simulation parameters:

Peer capabilities: bandwidth, number of shared files Peer behavior: online/offline periods, waiting time for

sessions Resource distribution: file sizes, popularity of files

Considered attack scenarios:Individual, collaborative and pseudonym changing attacks scenarios

Simulated nine different malicious behaviors

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8.1) Attacker Models for Simulation

2 types of attacks:1) Service-based attack — uploading a virus

infected or inauthentic file 2) Recommendation-based attack — giving

misleading recommendations Two subtypes of misleading recommendations:

Unfairly high recommendation: Giving a positively-biased trust value about the recommended peer

Unfairly low recommendation: Giving a negatively-biased trust value about the recommended peer

Three types of attackers:a) Individual attackersb) Collaboratorsc) Pseudospoofers

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a) Model of Individual Attackers

Individual attackers — perform attacks independently (does not cooperate with other attackers)

Three individual attacker behaviors: Naïve attacker — always uploads

infected/inauthentic files and gives unfairly low recommendations to others

Discriminatory attacker — attacks a selected group of victims

Always uploads infected/inauthentic files to them and gives unfairly low recommendations for them

It treats all other peers fairly

Hypocritical attacker — uploads infected/inauthentic files and gives unfairly low recommendations with x% probability

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b) Model of Collaborators

Collaborators — malicious peers that coordinate attacks with other peers

Collaborators never attack each other Always upload authentic files to each other Always give fair recommendations to other

collaborators

Collaborators always give unfairly high recommendations about each other to non-collaborating peers Try to convince good peers to download files from

any one of the collaborators

Three collaborator behaviors (analogous as for individual attackers) Naïve, Hypocritical, Discriminatory

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c) Model of Pseudospoofers

Pseudospoofer — a malicious peer which changes its pseudonym periodically to escape from being identified

A pseudospoofer behaviors: Naïve / discriminatory / hypocritical

Analogous to individual attacker behaviors

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8.2) Experimental Results

In a non-malicious network, reputation of a peer is proportional to its capabilities such as network bandwidth, average online period on the network and number of shared resources

In a malicious network, service and recommendation-based attacks affect reputation of a peer

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a) Results for Individual Attackers

All attacks of individual attackers are mitigated easily

Hypocritical attacks take more time to detect than other individual attackers

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b) Results for Collaborators (1)

Detection of collaborators usually takes longer than detection of an individual attacker Unfairly high recommendations provides an

advantage except naïve collaborators

Naïve collaborators do not benefit from collaboration They have zero reputation since they can not

complete any service interaction Hence they are not requested for any recommendations

Collaboration is partially successful in hypocritical and discriminatory behaviors

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b) Results for Collaborators (2)

Hypocritical collaborators succeeded to launch more service-based attacks at the start of experiments

At the start, good peers do not have many acquaintances - collaborators deceive them easily by distributing unfairly high recommendations for each other

Then collaborators able to take advantage of unfairly heightened reputations to attract good peers for their “services” (= attacks)

As good peers gain more good acquaintances, hypocritical collaborators are identified (and their attacks mitigated)

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b) Results for Collaborators (3)

Service-based attacks of discriminatory collaborators are mitigated easier than those of hypocritical ones

Victims of discriminatory collaborators quickly identify them

But discriminatory collaborators gained a high recommendation trust value & were able to continue distributing misleading recommendations

Collaborators do not attack most good peers Thus, good peers believe their recommendations

Victims give low recommendations for discriminatory collaborators

However, good peers think that victims are giving misleading recommendations for discriminatory collaborators

Thus, discriminatory collaborators are able to continue distributing misleading recommendations

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c) Results for Pseudospoofers

Attacks of pseudospoofers are as easily mitigated as those of individual attackers Peers gain more acquaintances and have less

tendency to select strangers with time Thus, pseudospoofers are more isolated from

good peers after each pseudonym change

Experimental results for Pseudospoofers

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d) Experim. Results – General Remarks

Defining a context of trust increases a peer's ability to identify and mitigate attacks on the context-related tasks

Context of trust can be used to increase a peer’s reasoning ability for different tasks

Such as routing, integrity checking and protecting privacy

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THE END