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Detecting and correcting malicious data in VANETs
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Source International Conference on Mobile Computing and Networking archive
Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks table of contents
Philadelphia, PA, USA
SESSION: Security in VANET table of contents
Pages: 29 - 37  
Year of Publication: 2004
ISBN:1-58113-922-5
Authors
Philippe Golle  Palo Alto Research Center
Dan Greene  Palo Alto Research Center
Jessica Staddon  Palo Alto Research Center
Sponsors
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In order to meet performance goals, it is widely agreed that vehicular ad hoc networks (VANETs) must rely heavily on node-to-node communication, thus allowing for malicious data traffic. At the same time, the easy access to information afforded by VANETs potentially enables the difficult security goal of data validation. We propose a general approach to evaluating the validity of VANET data. In our approach a node searches for possible explanations for the data it has collected based on the fact that malicious nodes may be present. Explanations that are consistent with the node's model of the VANET are scored and the node accepts the data as dictated by the highest scoring explanations. Our techniques for generating and scoring explanations rely on two assumptions: 1) nodes can tell "at least some" other nodes apart from one another and 2) a parsimony argument accurately reflects adversarial behavior in a VANET. We justify both assumptions and demonstrate our approach on specific VANETs.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  23

Collaborative Colleagues:
Philippe Golle: colleagues
Dan Greene: colleagues
Jessica Staddon: colleagues