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.

Meeting Name

IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2021 (2021: Jun. 7-11, Pisa, Italy)


Computer Science


This work is funded by NSF grants SATC-2030611, SATC-2030624, CNS-1818942, CNS-1545037, OAC-2017289 and DGE-1433659.

Keywords and Phrases

Cyber Deception; Mobile Crowdsensing Security; Moving Target Defense; Security of AI; Trust

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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Publication Date

11 Jun 2021