1 / 21

Algorithm

Algorithm. 0. Preliminaries. An AS a ’s neighbors: N a = [b,c,d, …, n] b i : i ’s base reputation downloaded from the server f i : feedback from a neighbor i α: weight value for observed evaluation (0≤α ≤ 1) β: weight value for neighbors’ feedbacks (0 ≤ β ≤ 1)

sidonia-orsin
Download Presentation

Algorithm

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Algorithm

  2. 0. Preliminaries • An AS a’s neighbors: Na = [b,c,d, …, n] • bi : i’s base reputation downloaded from the server • fi: feedback from a neighbor i • α: weight value for observed evaluation (0≤α≤1) • β: weight value for neighbors’ feedbacks (0≤β≤1) • h: history of providing feedbacks each AS has towards its neighbors in++ when a neighbor has provided a feedback out++ when an AS has provided a feedback to a neighbor h=in-out probability of providing a feedback to a neighbor (if 0, random decision)

  3. 1. Base reputation 1. Each AS downloads a base reputation from the server.(http://rtg.cis.upenn.edu/qtm/ascred/report.php) b a Ra=50 Rc=70 Re=90 c Rb=100 Rb=100 Re=90 e d Rb=100 Rc=70 Rd=90 Rf=30 Re=90 f Re=90

  4. 2. Observation 2. Observe each neighbor’s behavior for a time period and evaluate the services provided (evaluations will be discussed later). b a 1.3.0.0/16, [v, x, y, z] 1.2.0.0/16, [a] c 1.2.0.0/16 1.2.0.0/16, [a, b] 1.3.0.0/16, [v, x, y, z, c] e d f

  5. 3. Take a vote 3.At the time of evaluation, each AS takes a vote to its neighbors except the target agent. Neighbors can repeat the vote to their neighbors. b c d f b a Ra=50 Rc=70 Re=90 c Rb=100 Rb=100 Re=90 e d Rb=101 Rc=65 Rd=95 Rf=10 Re=90 f Re=90

  6. 3. Continued E.g. e asks about f to its neighbors (b, c, d). b is not sure about f and takes another vote to its neighbors (a, c). g b a c ask(f) ask(f) e ask(f) d f

  7. 3. Continued • Neighbors may decide not to cast a vote. • Neighbors are responsible for collecting feedbacks of the vote they took and propagate the result to the asker. • If the feedback is not in time, the node who asked for feedback might not include the feedback from the neighbor and the neighbor won’t get a credit. g ask(f) ask(f) b ask(f) a feedback feedback c feedback ask(f) ask(f) feedback e ask(f) d f

  8. 4. update Rf = αbE + (1-αb)V V = Σβifi/|N| oute++ ina++ Rf = αcE + (1-αc)V V = Σβkfk/|N| oute++ ing++ outc++ g outb++ ask(f) ask(f) b ask(f) a feedback feedback c feedback ask(f) ask(f) feedback |N|: number of neighbors who provided feedbacks β = <βi,βj, …., βn> βi+βj+…+βn =1 e ask(f) d Rf = αeE + (1-αe)V V = Σβifi/|N| inb++ inc++ f

  9. 5. broadcast • An AS can broadcast when it found a particularly bad behavior of its neighbor. • Weather to take the reporting AS’s opinion or not is up to its neighbors. • If some neighbors decide the reporting is worth to spread, they can broadcast by themselves.

  10. bucket brigade effect by history • Reputation cannot be propagated <- the network is not built on reputations • Nodes cannot explicitly exchange reputations <- distributed algorithm • History distributions could be used to track the characteristics of each ASes • History also indirectly triggers feedbacks b c a ask(b) ask(a) ask(b) d e ask(a)

  11. A New edge Download the base reputation Observe the behavior and Evaluate Take a vote and get feedbacks Update the reputation g Rc=70 b a Ra=50 Rc=70 Re=90 c Rb=100 Rb=100 Re=90 Rg=33 e d Rb=100 Rc=70 Rd=90 Rf=30 Re=90 f Re=90

  12. Resource • AS-CRED: http://rtg.cis.upenn.edu/qtm/ascred/main.php • Prefix AS mapping: http://www.caida.org/data/routing/routeviews-prefix2as.xml

  13. evaluation of service For each service s = {prefix, AS_PATH},where AS_PATH = [AS1, AS2, … , ASN] • Check if the originator of the prefix owns it.-if it doesn’t punish all the neighbors in the path-may broadcast the originator • Prevalence and persistence • Valley free: infer relationship -> check validity • infected IPs

  14. DATa • Prefix to AS mapping dataset • Internet Traces 2012 • BGP update data

  15. feedback • [-1, 0, 1]

  16. P2P simulator • Trace Generator • Trace Simulator (trace file, trust algorithm, strategy)

  17. Base Reputation • http://rtg.cis.upenn.edu/qtm/ascred/main.php

  18. beta distribution • [β1,β2,β3, …,βn] • βk= Rk/(R1+…+Rn) • Only distributed to the neighbors with R > 0. • if h=0, i.e. in == out, the probability of providing a feedback is 50%. • -1 < R < 1

  19. summary • Download the base reputation and convert. • Observe the services for a given time period. • Broadcast when needed. • Evaluate the services and take votes. • With feedbacks and the evaluation, update reputations of neighbors. • Adjust beta and alpha values as needed.

  20. Case 1 A node, in this case ‘c’, gets more influence. Assuming each node uses same alpha values, and assuming the node ‘a’ uses same beta values for all the neighbors, ‘c’ gets α(1-α)2 discounts when ‘b’, ‘d’, ‘e’, ‘f’ get α(1-α)/4 discount. In general, Lth neighbor contributes α(1-α)L b d c a e f

  21. case 2 The vote comes around to the node who started it. -> When taking a vote, a node should include the ‘originator’ and ‘target’. -> Or an originator of the vote generates an ID for each vote and when it comes around to itself, it knows itself generated the vote. b c a f d e

More Related