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A reputation based recommendation system over trusted nodes( iTrust )

A reputation based recommendation system over trusted nodes( iTrust ). Chirag Gupta Isha Agarwal Avinash Kautham. Outline. Introduction Query information – Two approaches On Demand Approach Exchange Trust List approach Advantages and Disadvantages of each Evaluation Metrics

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A reputation based recommendation system over trusted nodes( iTrust )

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  1. A reputation based recommendation system over trusted nodes(iTrust) • Chirag Gupta • IshaAgarwal • AvinashKautham

  2. Outline • Introduction • Query information – Two approaches • On Demand Approach • Exchange Trust List approach • Advantages and Disadvantages of each • Evaluation Metrics • Results (expected)

  3. 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.

  4. 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.

  5. New Node recommendation Yes Scanning New node detected Do you know(trust) this guy ? Yes New node is Recommended

  6. Query information for new node • Two Approaches • Exchange trust-list Approach • Global Model • Uno Model • On Demand Approach • Global Model • UnoModel

  7. 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.

  8. Score Calculation phase • Trust score calculation for each new node. • If trust score is greater than a threshold, new node is recommended.

  9. 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)

  10. 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

  11. 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)

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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 ??

  17. 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.

  18. 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 ).

  19. 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

  20. 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.

  21. Eg: Node 1 TL org Node 1 Lex 2 0.4 2 4 0.5 3 0.6 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.

  22. Eg: Node 1 Tlorg(id,trust,rep) Node 1 Lex 2 0.4 0.5 (2)/3 4 (0.5)/0.3 3 0.6 0.4 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) ?

  23. 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.

  24. 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.

  25. 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.

  26. Exchange Trust-list • Advantages: • Works when there is no trusted node around. • Network traffic is lesser or packets transmitted per request is lesser.

  27. 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]

  28. 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.

  29. Evaluation Metrics • Epidemic Routing[1] • Unreachability • Overhead • Delay

  30. 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.

  31. References • [1] "Proximity based Trust-Advisor using encounters for Mobile Societies:    Analysis of four Filters" -  Udayan Kumar, GautamThakur 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=5935316 )

  32. 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_final.pdf ) • [5] “ DEFENDING AGAINST MALICIOUS NODES USING AN SVM BASED REPUTATION SYSTEM“ -RehanAkbani, TurgayKorkmaz, and G. V. S. RajuUniversity of Texas at San Antonio San Antonio,TX, USA

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