On Data-Centric Trust Establishment in Ephemeral Ad Hoc Networks Maxim Raya, Panos Papadimitratos, Virgil D. Gligor, Jean-Pierre Hubaux INFOCOM 2008.

Slides:



Advertisements
Similar presentations
June 4, 2004 A Robust Reputation System for P2P and Mobile Ad-hoc Networks Sonja Buchegger 1 A Robust Reputation System for P2P and Mobile Ad-hoc Networks.
Advertisements

Modelling uncertainty in 3APL Johan Kwisthout Master Thesis
Evaluation of standard ICES stock assessment and Bayesian stock assessment in the light of uncertainty: North Sea herring as an example Samu MäntyniemiFEM,
Conceptual Framework for Dynamic Trust Monitoring and Prediction Olufunmilola Onolaja Rami Bahsoon Georgios Theodoropoulos School of Computer Science The.
EPFL, Lausanne, Switzerland Márk Félegyházi Equilibrium Analysis of Packet Forwarding Strategies in Wireless Ad Hoc Networks – the Static Case Márk Félegyházi.
Rulebase Expert System and Uncertainty. Rule-based ES Rules as a knowledge representation technique Type of rules :- relation, recommendation, directive,
DETC06: Uncertainty Workshop; Evidence & Possibility Theories Evidence and Possibility Theories in Engineering Design Zissimos P. Mourelatos Mechanical.
Self-Organized Anonymous Authentication in Mobile Ad Hoc Networks Julien Freudiger, Maxim Raya and Jean-Pierre Hubaux SECURECOMM, 2009.
TAODV: A Trusted AODV Routing Protocol for MANET Li Xiaoqi, GiGi March 22, 2004.
A Survey of Trust Management for Mobile Ad Hoc Networks
Application of Bayesian Network in Computer Networks Raza H. Abedi.
Uncertainty Everyday reasoning and decision making is based on uncertain evidence and inferences. Classical logic only allows conclusions to be strictly.
Using Game Theoretic Approach to Analyze Security Issues In Ad Hoc Networks Term Presentation Name: Li Xiaoqi, Gigi Supervisor: Michael R. Lyu Department:
Dempster-Shafer Theory From Buchanan-Shortliffe-1984/Chapter- 13.pdf.
5/17/20151 Probabilistic Reasoning CIS 479/579 Bruce R. Maxim UM-Dearborn.
Modeling Human Reasoning About Meta-Information Presented By: Scott Langevin Jingsong Wang.
An Approach to Evaluate Data Trustworthiness Based on Data Provenance Department of Computer Science Purdue University.
A Robust Process Model for Calculating Security ROI Ghazy Mahjub DePaul University M.S Software Engineering.
Trust Level Based Self-Organized Routing Protocol for Secure Ad Hoc Networks Li Xiaoqi, GiGi 12/3/2002.
TAODV: A Trust Model Based Routing Protocol for Secure Ad Hoc Networks Li Xiaoqi, GiGi October 28, 2003.
TAODV: A Trust Model Based Routing Protocol for Secure Ad Hoc Networks Xiaoqi Li, Michael R. Lyu, and Jiangchuan Liu IEEE Aerospace Conference March 2004.
16722 Sensing and Sensors Mel Siegel )
Dempster-Shafer Theory SIU CS 537 4/12/11 and 4/14/11 Chet Langin.
Kemal AkkayaWireless & Network Security 1 Department of Computer Science Southern Illinois University Carbondale CS 591 – Wireless & Network Security Lecture.
Representing Uncertainty CSE 473. © Daniel S. Weld 2 Many Techniques Developed Fuzzy Logic Certainty Factors Non-monotonic logic Probability Only one.
Information Fusion Yu Cai. Research Paper Johan Schubert, “Clustering belief functions based on attracting and conflicting meta level evidence”, July.
Lecture 05 Rule-based Uncertain Reasoning
Integrated Social and Quality of Service Trust Management of Mobile Groups in Ad Hoc Networks Ing-Ray Chen, Jia Guo, Fenye Bao, Jin-Hee Cho Communications.
Routing Security in Wireless Ad Hoc Networks Chris Zingraf, Charisse Scott, Eileen Hindmon.
Revocation Games in Ephemeral Networks Maxim Raya, Mohammad Hossein Manshaei, Márk Félegyházi, Jean-Pierre Hubaux CCS 2008.
On Data-Centric Trust Establishment in Ephemeral Ad Hoc Networks Maxim Raya, Panagiotis Papadimitratos, Virgil D. Gligory, Jean-Pierre Hubaux Presented.
Study group Junction SHERLOCK IS AROUND: DETECTING NETWORK FAILURES WITH LOCAL EVIDENCE FUSION Qiang Ma 1, Kebin Liu 2, Xin Miao 1, Yunhao Liu.
Fuzzy Trust Recommendation Based on Collaborative Filtering for Mobile Ad-hoc Networks Junhai Luo 1,2, Xue Liu 1, Yi Zhang 3,Danxia Ye 2,Zhong Xu 1 1 McGill.
Bayesian Learning By Porchelvi Vijayakumar. Cognitive Science Current Problem: How do children learn and how do they get it right?
Trust Model Based Self-Organized Routing Protocol For Secure Ad Hoc Networks Li Xiaoqi CSE Department, CUHK 29/04/2003.
On the Tradeoff between Trust and Privacy in Wireless Ad Hoc Networks Maxim …...…. Raya Reza …….…. Shokri Jean-Pierre..Hubaux LCA1, EPFL, Switzerland The.
Trust- and Clustering-Based Authentication Service in Mobile Ad Hoc Networks Presented by Edith Ngai 28 October 2003.
Bayesian Networks for Data Mining David Heckerman Microsoft Research (Data Mining and Knowledge Discovery 1, (1997))
Bayesian Statistics and Belief Networks. Overview Book: Ch 13,14 Refresher on Probability Bayesian classifiers Belief Networks / Bayesian Networks.
A study of Intelligent Adaptive beaconing approaches on VANET Proposal Presentation Chayanin Thaina Advisor : Dr.Kultida Rojviboonchai.
Estimating Component Availability by Dempster-Shafer Belief Networks Estimating Component Availability by Dempster-Shafer Belief Networks Lan Guo Lane.
1 Computing Trust in Social Networks Huy Nguyen Lab seminar April 15, 2011.
Uncertainty Management in Rule-based Expert Systems
High-integrity Sensor Networks Mani Srivastava UCLA.
Dual-Region Location Management for Mobile Ad Hoc Networks Yinan Li, Ing-ray Chen, Ding-chau Wang Presented by Youyou Cao.
Fall  Types of Uncertainty 1. Randomness : Probability Knowledge about the relative frequency of each event in some domain Lack of knowledge which.
The Representation of Uncertainty for Validation and Analysis of Social Simulations TRADOC Analysis Center – Monterey 21 September 2010 Unclassified Debbie.
A Quantitative Trust Model for Negotiating Agents A Quantitative Trust Model for Negotiating Agents Jamal Bentahar, John Jules Ch. Meyer Concordia University.
Dynamic Trust Models for Ubiquitous Computing Environments Colin English, Paddy Nixon, Sotirios Terzis, Andrew McGettrick, Helen Lowe Department of Computer.
Prof. J.-P. Hubaux Mobile Networks Module I – Part 2 Securing Vehicular Networks 1.
Opportunistic MANETs: Mobility Can Make Up for Low Transmission Power.
Semantic Web Knowledge Fusion Jennifer Sleeman University of Maryland, Baltimore County Motivation Definitions Methodology Evaluation Future Work Based.
International Conference on Fuzzy Systems and Knowledge Discovery, p.p ,July 2011.
A Security Framework with Trust Management for Sensor Networks Zhiying Yao, Daeyoung Kim, Insun Lee Information and Communication University (ICU) Kiyoung.
Computer Science and Engineering 1 Mobile Computing and Security.
Ahmad Salam AlRefai.  Introduction  System Features  General Overview (general process)  Details of each component  Simulation Results  Considerations.
- 1 - Computer model under uncertainty In previous lecture on accuracy assessment –We considered mostly deterministic models. – We did not distinguish.
MITRE 7 April 2009 CS 5214 Presenter: Phu-Gui Feng Performance Analysis of Distributed IDS Protocols for Mobile GCS Dr. Jin-Hee Cho, Dr. Ing-Ray Chen MITRE.
Optimizing the Location Obfuscation in Location-Based Mobile Systems Iris Safaka Professor: Jean-Pierre Hubaux Tutor: Berker Agir Semester Project Security.
Risk-Aware Mitigation for MANET Routing Attacks Submitted by Sk. Khajavali.
Advisor: Prof. Han-Chieh Chao Student: Joe Chen Date: 2011/06/07.
Presented by Edith Ngai MPhil Term 3 Presentation
TAODV: A Trusted AODV Routing Protocol for MANET
Recommendation Based Trust Model with an Effective Defense Scheme for ManetS Adeela Huma 02/02/2017.
Giannis F. Marias, Vassileios Tsetsos,
Dealing with Uncertainty
Wenjia Li Anupam Joshi Tim Finin May 18th, 2010
Representing Uncertainty
Ariadne: A Secure On-Demand Routing Protocol for Ad Hoc Networks
A Trust Evaluation Framework in Distributed Networks: Vulnerability Analysis and Defense Against Attacks IEEE Infocom
Presentation transcript:

On Data-Centric Trust Establishment in Ephemeral Ad Hoc Networks Maxim Raya, Panos Papadimitratos, Virgil D. Gligor, Jean-Pierre Hubaux INFOCOM 2008

Ephemeral networks Definition No prior associations Short-lived contacts Volatile environment Example: VANET Trust Properties Trust in entities can be pre- established Reputation is hard to build Trust in data is important Trust establishment needs to be rethought 2

What is data trust?

Data Trust in Networks Packet forwarding Security associations Reputation A M B Data dissemination Insufficient Hard 4 Traditional ad hoc networksEphemeral networks Data Trust = Entity TrustData Trust = F(Entity Trust, context)

Event-specific trust Dynamic trust metric Security status A C B M General Framework Trust Computation Weights (data-centric trust levels) is the default trustworthiness Location Time Event reports of type from nodes

A C B M General Framework Evidence Evaluation Decision Logic Evidence Evaluation Output: Decision on Reported Event Evidence Event reports of type from nodes

Decision Logics (1) Most trusted report

Decision Logics (2) Most trusted report Weighted voting

Decision Logics (3) Most trusted report Weighted voting Bayesian inference – Takes into account prior knowledge

Decision Logics (4) Most trusted report Weighted voting Bayesian inference Dempster-Shafer Theory – probability is bounded by belief and plausibility – Uncertainty (lack of evidence) does not refute nor support evidence

Decision Logics (4) Most trusted report Weighted voting Bayesian inference Dempster-Shafer Theory 11 basic belief assignment trust level event report on event

Decision Logics (4) Most trusted report Weighted voting Bayesian inference Dempster-Shafer Theory 12 Dempster’s rule for combination: supporting evidence conflicts

Case Study: VANET 13 Data Trust Decision on event

Performance comparison MATLAB and ns2 100 simulation runs 95% confidence intervals Broadcast environment

Effect of Data Trust (1) Honest nodes (0.8) are more trustworthy than attackers (0.6)

Effect of Data Trust (2) Honest nodes (0.6) are less trustworthy than attackers (0.8)

Effect of Uncertainty Honest nodes (0.4) are more trustworthy than attackers (0.2)

Evolution in Time Highway scenario; 50% of reports are false (received first); Honest nodes (0.8) are more trustworthy than attackers (0.6)

Conclusions Trust in traditional MANETs pertains exclusively to entities Data-centric trust is more representative and useful in ephemeral networks (e.g., VANETs) Several decision logics can be used, notably: – Bayesian inference if there is prior knowledge – Dempster-Shafer Theory if there is uncertainty

Effect of Prior Knowledge 10 nodes vs. 50 before