Resilience Against Bad Mouthing Attacks in Mobile Crowdsensing Systems via Cyber Deception
Mobile Crowdsensing System (MCS) applications deploy rating feedback mechanisms to help quantify the trustworthiness of published events which over time improve decision accuracy and establish user reputation. In this paper, we first show that factors such as sparseness, inherent error probabilities of rating feedback labelers, and prior knowledge of the event trust scoring models, can be used by strategic adversaries to hijack the feedback labeling mechanism itself with bad mouthing attacks. Then, we propose a randomized rating sub-sampling technique inspired from moving target defense and cyber deception to mitigate the degradation in the resulting event trust scores of truthful events. We offer a game theoretic strategy under various knowledge levels of an adversary and the MCS in regards to picking an optimal sub-sample size for bad mouthing attacks and event trust calculations respectively, by using a vehicular crowdsensing as a proof-of-concept.
P. Roy et al., "Resilience Against Bad Mouthing Attacks in Mobile Crowdsensing Systems via Cyber Deception," Proceedings of the IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (2021, Pisa, Italy), pp. 169-178, article no. 9469467, Institute of Electrical and Electronics Engineers (IEEE), Jun 2021.
The definitive version is available at https://doi.org/10.1109/WoWMoM51794.2021.00030
IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2021 (2021: Jun. 7-11, Pisa, Italy)
Keywords and Phrases
Cyber Deception; Mobile Crowdsensing Security; Moving Target Defense; Security of AI; Trust
International Standard Book Number (ISBN)
Article - Conference proceedings
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11 Jun 2021