Multinomial Trust In Presence Of Uncertainty And Adversaries In Dsa Networks
Keywords
Bayes methods; Channel estimation; Collaboration; Computational modeling; Computer security; Sensors; Uncertainty
Abstract
Dynamic spectrum access (DSA) networks allow opportunistic spectrum access to license exempt secondary nodes. Usually secondary nodes employ a cooperative sensing mechanism to correctly infer spectrum occupancy. However, the possibility of falsification of locally sensed occupancy report, also known as secondary spectrum data falsification (SSDF) can cripple the operation of secondary networks. In this paper, we propose a multivariate Bayesian trust model for secondary nodes in a distributed DSA network. The proposed model accurately incorporates anomalous behavior as well as monitoring uncertainty that might arise from an anomaly detection scheme. We also propose possible extensions to the SSDF attack techniques. Subsequently, we use a machine learning approach to learn the thresholds for classifying nodes as honest or malicious based on their trust values. The threshold based classification is shown to perform well under different path loss environments and with varying degrees of attacks by the malicious nodes. We also show the trust based fusion model can be used by nodes to disregard a node's information while fusing the individual reports. Using the fusion scheme, we report the improvements of cooperative spectrum decisions for various multi-channel SSDF techniques.1
Publication Date
12-14-2015
Publication Title
Proceedings - IEEE Military Communications Conference MILCOM
Volume
2015-December
Number of Pages
611-616
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/MILCOM.2015.7357511
Copyright Status
Unknown
Socpus ID
84959282478 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84959282478
STARS Citation
Bhattacharjee, Shameek; Chatterjee, Mainak; Kwiat, Kevin; and Kamhoua, Charles, "Multinomial Trust In Presence Of Uncertainty And Adversaries In Dsa Networks" (2015). Scopus Export 2015-2019. 2022.
https://stars.library.ucf.edu/scopus2015/2022