Trust management in mobile ad hoc networks using a scalable maturity based model
Download
1 / 49

Trust Management in Mobile Ad Hoc Networks Using a Scalable Maturity-Based Model - PowerPoint PPT Presentation


  • 131 Views
  • Uploaded on

Trust Management in Mobile Ad Hoc Networks Using a Scalable Maturity-Based Model. Authors: Pedro B. Velloso , Rafael P. Laufer , Daniel de O. Cunha, Otto Carlos M. B. Duarte, and Guy Pujolle. Paper Presentation By : Gaurav Dixit ([email protected]). Outline. Introduction Trust Model

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Trust Management in Mobile Ad Hoc Networks Using a Scalable Maturity-Based Model' - kasa


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Trust management in mobile ad hoc networks using a scalable maturity based model

Trust Management in Mobile Ad Hoc Networks Using a Scalable Maturity-Based Model

Authors: Pedro B. Velloso, Rafael P. Laufer, Daniel de O. Cunha, Otto Carlos M. B. Duarte, and Guy Pujolle

Paper Presentation By : Gaurav Dixit ([email protected])


Outline Maturity-Based Model

  • Introduction

  • Trust Model

  • Implementation

  • Results


Introduction Maturity-Based Model

  • MANets - same node can work as router server client

  • Assumption of good behavior – Not true!

  • Trust needs to be measured - This paper provides one such method.

  • Applying human trust dynamics to trust calculation of nodes

  • Builds on recommendations





Relationship Maturity networks:

Similar to human trust behavior, more weightage is given to the recommendations from older neighbors.


  • Trust Model networks:

  • Trust level assigned to each neighbor.

  • Trust value reflects behavior history, and thus expected future behavior.

  • Node forms opinion based on experiences.

  • Transmission of these opinions about node i are called recommendations.


Trust Model … networks:

Recommendations compensate for lack of monitoring capabilities.

Paper defines Recommendation Exchange Protocol (REP)


  • Trust Model… networks:

  • Trust level varies from 0 to 1.

  • Recommendation from C more important than that from B, because of relationship maturity.


Trust Model: Architecture networks:

Two parts:

Learning Plan: gathers and converts information into knowledge.

Trust plan: assess trust level of each neighbor using stored knowledge and recommendations.



  • Trust Model: Components networks:

  • Behavior monitor observes network, indicates new neighbors to Rec Manager, and send behavior report to Classifier.

  • Classifier sends behavior classification to Experience Calculator.

  • Trust Calculator calculates trust with inputs from experiences and recommendations.

  • Auxiliary Trust Table entries correspond to relationship maturity.

  • Trust table entries have timeout.


  • Trust Model: Components networks:

  • Three operation modes:

  • Simple: Just trust table, REP optional

  • Intermediate: Simple mode plus storage of recommendations

  • Advanced: Complete system implementation.

  • Recommendation Manager implements REP.

  • All nodes are in advanced mode in this paper.


Trust level evaluation networks:

𝑇𝑎(𝑏) = (1 − 𝛼)𝑄𝑎(𝑏) + 𝛼𝑅𝑎(𝑏)

𝑄𝑎(𝑏) = 𝛽𝐸𝑎(𝑏) + (1 − 𝛽)𝑇𝑎(𝑏)

Ta(b) ->Trust calculation from node a for node b

Qa(b) -> Personal Experience

Ra(b) -> Recommendations

All variables(except a & b) range from 0 to 1.


Recommendation Computation networks:

𝐾𝑎 subset of neighbors

𝑀𝑖(𝑏)  relationship Maturity

𝑋𝑖(𝑏)  random variable with normal distribution representing recommendation uncertainty.

𝑋𝑖(𝑏) = 𝑁(𝑇𝑖(𝑏), 𝜎𝑖(𝑏))


First Trust Values networks:

Initial trust values can be:

Prudent : Strangers have low trust value

Optimist: High trust in new neighbors.

Moderate: Trust value between Prudent and optimist.

Fa First trust value

𝑇𝑎(𝑏) = (1 − 𝛼)𝐹𝑎 + 𝛼𝑅𝑎(𝑏)


Recommendation Exchange Protocol networks:

Only one hop neighbors considered. ( IP TTL=1)

Consists of:

TREQ: Trust Request

TREP: Trust Reply

TA: Trust Advertisement


REP networks:

TREQ sent when nodes first meet, with IP of new neighbor as target node. Wait time tREQ before sending TREQ

TREP sent by neighbors who have target node as their neighbor, after waiting for random time period tREP

TA sent if trust level changes by threshold 𝜋


Authentication networks:

A pair of public-private key for each node is sufficient for the system to work.

Sybil attack would not be a problem since the malicious identities are quickly found and ignored.


Trust Model Implementation networks:

Learning Plan


Nature of nodes vary from 0 (untrustworthy) to 1 (trustworthy)

A node with nature of 0.8 would do 8 good actions out of 10.

Behavior Monitor is emulated by concept of perception, which indicates probability of noticing a certain action.

Classifier (perfectly) classifies actions.


Node will decide for itself whether or not it will use behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Experience Calculator observes imin actions before calculating trust. Higher perception would result in more accurate trust level. But higher imin means higher convergence time.

Paper assumes imin =10


Results: Small networks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

All nodes are at one hop distance.

Time in seconds.

Convergence at t=350 for 𝛼 = 𝛽 = 𝜏 = 0.5


Results: Small networks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Optimistic first trust strategy.

Time in minutes.

Nature set to 0.2 .

Number of neighbors varied.


Results: Small networks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Neighbors =15

Varying alpha


Results: Small networks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Perception 𝜏 is the fraction of actions a node can notice from its neighbors

Varying 𝜏


Results: behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.MultihopMANets

Analyzing movement in more complex networks.

21 nodes with 250m transmission range, placed in 1000 m × 400 m .

𝛼 = 𝛽 = 𝜏 = 0.5

First trust optimist (0.9)

Nature of nodes = 0.2


Results: behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.Multihop …

m1 keeps 3 old neighbors

m2 has no old neighbors


Results: behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.Multihop …

m1 keeps 3 old neighbors

m2 has no old neighbors


Results: behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.Multihop …

Node speeds three times faster.


Results: behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.Multihop …

Varying perception – lower perception takes longer time to converge.


Results: Relationship maturity behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Node 1,8,15 go to zone F2.

Evaluating trust level of node 8 about node 20


Results: Relationship maturity behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Using lower perception value(0.2)

Note that recommendations are important in low perception cases


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

20 nodes -250m transmission range, placed in a

150 m × 150m

Node 1 changes nature from 0.9 to 0.2


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Malicious nodes fixed at 40%


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Slander Attack

Node2 evaluating node1 which has nature 0.9

Pessimistic strategy (Fa=0.1)


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Slander Attack

Varying alpha


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Slander Attack

Varying perception parameter.


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Slander Attack

Malicious nodes lie after t=200


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Slander Attack

Malicious nodes identification time varying


Results: Lying Attacks behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Slander Attack

Malicious nodes identification time varying


  • REP behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

  • To reduce number of messages sent across network:

  • TREQ is sent once containing multiple target nodes, using timer based approach.

  • TREP instead of sending once per request, implemented as broadcast – this saves 85%

  • TREP implemented, additionally, with timer, saves 99% messages.

  • TA implemented with a threshold to reduce its occurrence.


REP behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.


REP behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Changing the value of Trust threshold(𝜋)


REP behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

Changing the value of Trust threshold(𝜋) and its impact on trust levels.


Discussion behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

  • Using smart timers in suppressing redundant messages scales well in large networks, reducing overhead for trust management by 85 to 99%.

  • Increasing value of α improves the trust model efficiency, since we can use already derived results (by neighbors) in the form of recommendations.


Conclusion behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.

  • Flexible trust evaluation model proposed based on concept of human trust, which uses recommendations and relationship maturity.

  • Recommendation Exchange Protocol (REP) proposed.

  • Model highly scalable – since only neighbors consulted.

  • Model tolerates 35 % liars

  • Trust level error reduced by 50% by using relationship maturity parameter.


Thank You! behavior monitor in promiscuous mode. Required perception value and personal constraints would help in this decision.


ad