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A reputation based recommendation
system over trusted nodes(iTrust)
 Chirag Gupta
 Isha Agarwal
 Avinash Kautham
Outline
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Introduction
Query information – Two approaches
On Demand Approach
Exchange Trust List approach
Advantages and Disadvantages of each
Evaluation Metrics
Results (expected)
Introduction
 Need of trust in MANET
 Lack of infrastructure leads to the greater possibility of
presence of malicious nodes in MANETs.
 This becomes a psychological barrier for users in
communicating Peer to Peer in MANETs
 Establishing trust between known users will help to
break this barrier.
Related work
 iTrust – Encounter Based Trust[1]
 Location Vector, Duration Vector
 Bayesian based approach[2]
 Uses first hand observation and propagate this
information over the network
 SVM based approach[6]
 Uses machine learning approaches to train network with
all possible attack scenarios.
New Node recommendation
Yes
Scanning
New node detected
Do you know(trust) this guy ?
New node is
Recommended
Yes
Query information for new node
 Two Approaches
 Exchange trust-list Approach


Global Model
Uno Model
 On Demand Approach


Global Model
Uno Model
Two phases
 Scanning phase
 Scans all the mobile devices around the user using
bluetooth.
 Nodes found either be already TRUSTED node or
UNTRUSTED(new) Node.
 Score Calculation phase
 Trust score calculation for each new node.
 If trust score is greater than a threshold, new node is
recommended.
General Problems/Challenges
 How to query new node information to trusted node ?
 Once you get the information how to calculate the trust
score ?
 How to decide on threshold (minimum) score needed
for recommendation ?
 What if a particular user does not want to reveal its trust
list ?? (Privacy issues)
Calculation
 Global Model:
TSg n,u = ∑(TS ri,u * TSn,ri ) / N ∀ i = 1..N
 Uno Model:
TSn,u=PFr * PFn * REPr * TSr,u*TSn,r
On-Demand Query
 When a new node is encountered, all trusted nodes
present in the vicinity are queried for this new node.
 Privacy factor of new node and the recommender node
is considered.
 Calculation for trust score.
 TSn,u=PFr * PFn * REPr * TSr,u*TSn,r(Selfish model)
 Two approaches for how to get information of new
node
 GLOBAL model

Query every trusted node in vicinity.

Calculate the trust score for new from each trusted
node and take average of them.
 Uno model

Query the nodes in order.

Order can be decided on any one of below parameter.
 Higher Trust score
 Higher reputation ratings .
 Combination of both .

As soon as it finds new node in a trusted node’s trust list and
its calculate trust score is greater that threshold its
recommended.
Exchange Trust Lists Approach
 Trusts lists are exchanged between trusted users.
 Every user maintains its own trust list and a trust list
corresponding to every node present in its trust list.
 When a new node encountered user queries to the
trust lists maintained by itself.
 Calculate the trust score of the new node found and
recommend if greater than threshold.
Design Issues – Exchange Trust lists
 How to maintain the trust lists data ( Format )
 When do we exchange the trust list ??
 When to update the trusts lists ??
Exchange Method – When and How?
 Two possibilities
 When user adds a node to his trust, its trust list is
transferred.

Problems :- What if trusted node is not around ?
Transfer is not possible.
 Whenever user encounters a trusted node, trusts
lists are transferred or updated. ( after trust is
established)

Problems:- Most likely user will encounter trusted
nodes more often. Trusts lists keep updating (
Overhead ).
Overcoming issues (After List is got..)
 User keeps a list for every node.
 So user will maintain the recommender node id, its
trust list nodes and trust score.
Eg: Trust list of Node 2 (3 – 0.6, 4 – 0.5)  Node 1.
Node 1 will have : 2 3 0.6
2 4 0.5
 So, every node will have original trust list ( with
existing filters) TL org, trust list (our approach) TL new,
list of exchanged trusted nodes Lex.
 In the case of the approaches, storage will be slightly
different, there will just be a 1-1 mapping in the case of
Uno model here.
 Eg: Node 1 TL org
2 0.4
3 0.6
Node 1 Lex
2 4 0.5
2 5 0.4
3 4 0.3
 There will be duplicates in the case of the Global
Model because we need to get information from all the
trusted nodes, but NO DUPLICATES in case of Uno
model.
 Eg: Node 1 Tlorg(id,trust,rep)
2 0.4 0.5
3 0.6 0.4
Node 1 Lex
(2)/3 4 (0.5)/0.3
2
5 0.4
 Which value will you choose ? (Uno model)
 2 because it has higher reputation ?
 3 because it has higher trust ?
 3 because it has higher combined trust(rep*trust) ?
Overall Steps
 Establish trust list with the existing filters (iTrust)
 Scanning around
 Follow any one of the approaches for getting the trust
score and recommendation
 Add to the secondary Trust list maintained.
On-Demand Approach
 Disadvantages
 What if there is no new node in vicinity ?
 Overhead as every time new node is encountered its
queried over MANET.
 Advantages
 Minimal storage required in terms of storing trust list.
 If node density of MANET is high this simple approach
is expected to work better.
Exchange Trust-list
 Advantages:
 Works when there is no trusted node around.
 Network traffic is lesser or packets transmitted per
request is lesser.
 Disadvantages
 Lot of memory is required.
 Update of trust is very costly because after trust list is
exchanged, if user changes value of trust then it needs to be
updated.
[ need to periodically exchange list – Timestamp]
 More vulnerable to network security issues because
more information is sent over the network about the
trust list of a node.
 There can be a node which keeps asking or monitoring
all the other nodes without giving information.
Evaluation Metrics
 Epidemic Routing[1]
 Unreachability
 Overhead
 Delay
Results (expected):
 Global Model has more overhead and delay than
Selfish Model
 The Exchange trust-list will perform better when node
density is less in a particular area.
 The On-Demand approach will perform better when
node density is higher.
References
 [1] "Proximity based Trust-Advisor using encounters for Mobile Societies:
Analysis of four Filters" - Udayan Kumar, Gautam Thakur and Ahmed Helmy
Department of Computer and Information Science and Engineering,
University of Florida, FL, U.S.A.
(http://www.cise.ufl.edu/~gsthakur/docs/proximity.pdf)
[2] "A Robust Reputation System for Mobile Ad-hoc Network"
- Sonja Buchegger, Jean-Yves Le Boudec EPFL-IC-LC
(http://mescal.imag.fr/membres/corinne.touati/Sandra/robust_report.pdf)
 [3] “ A Reputation-based Scheme against Malicious Packet Dropping for
Mobile Ad Hoc Networks “ -Song JianHua, Ma ChuanXiang
School of Mathematics and Computer Science, Hubei University, Wuhan
430062, Hubei, China
(http://ieeexplore.ieee.org.lp.hscl.ufl.edu/stamp/stamp.jsp?tp=&arnumber=59
35316 )
References
 [4] “ Trust Modelling and Evaluation in Ad-hoc Networks
“- Yan Sun,Wei Yu, Zhu Han and K.J.Ray Liu
(http://www.ele.uri.edu/nest/paper/Globecom05Trust_fin
al.pdf )
 [5] “ DEFENDING AGAINST MALICIOUS NODES USING AN SVM
BASED REPUTATION SYSTEM “ -Rehan Akbani, Turgay Korkmaz,
and G. V. S. Raju
University of Texas at San Antonio San Antonio,TX, USA