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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@example.com). Outline. Introduction Trust Model
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Authors: Pedro B. Velloso, Rafael P. Laufer, Daniel de O. Cunha, Otto Carlos M. B. Duarte, and Guy Pujolle
Paper Presentation By : Gaurav Dixit (firstname.lastname@example.org)
Trust level of a node depends on:= (previous individual experiences) + (recommendation from neighbors)
Similar to human trust behavior, more weightage is given to the recommendations from older neighbors.
Recommendations compensate for lack of monitoring capabilities.
Paper defines Recommendation Exchange Protocol (REP)
Learning Plan: gathers and converts information into knowledge.
Trust plan: assess trust level of each neighbor using stored knowledge and recommendations.
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.
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))
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)
Only one hop neighbors considered. ( IP TTL=1)
TREQ: Trust Request
TREP: Trust Reply
TA: Trust Advertisement
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
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.
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
All nodes are at one hop distance.
Time in seconds.
Convergence at t=350 for 𝛼 = 𝛽 = 𝜏 = 0.5
Optimistic first trust strategy.
Time in minutes.
Nature set to 0.2 .
Number of neighbors varied.
Perception 𝜏 is the fraction of actions a node can notice from its neighbors
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
Node speeds three times faster.
Varying perception – lower perception takes longer time to converge.
Node 1,8,15 go to zone F2.
Evaluating trust level of node 8 about node 20
Using lower perception value(0.2)
Note that recommendations are important in low perception cases
20 nodes -250m transmission range, placed in a
150 m × 150m
Node 1 changes nature from 0.9 to 0.2
Malicious nodes fixed at 40%
Node2 evaluating node1 which has nature 0.9
Pessimistic strategy (Fa=0.1)
Varying perception parameter.
Malicious nodes lie after t=200
Changing the value of Trust threshold(𝜋)
Changing the value of Trust threshold(𝜋) and its impact on trust levels.