Ensemble Learning Against Adversarial AI-driven Fake Task Submission in Mobile Crowdsensing
Yueqian Zhang, Murat Şimşek, Burak Kantarcı · 2020
Non-dedicated nature of mobile crowdsensing (MCS) systems introduces vulnerabilities for MCS platforms in terms of sensing, computing, storage, and battery resources. The advent of adversarial artificial intelligence (AI) leads to high impact malicious behavior when adversaries aim to clog the resources of such a non-dedicated and ubiquitous system. This paper proposes an ensemble learning-based methodology for MCS platforms in order to mitigate the impacts of adversarial AI-driven fake task submission attacks, which are intelligently designed so to clog resources such as batteries, sensing, or memory resources. We validate our proposal through realistic simulations to generate crowdsensing data under two different cities, and intelligent fake task submissions under adversarial self-organizing maps. The experimental results show that when the submitted tasks undergo a Gradient Boosting-based classifier prior to being assigned to participants, the proposed solution can introduce battery savings at the participant devices up to 23%, and the impacted recruit population can be reduced from 24% to 6% whereas the defense mechanism can achieve an overall accuracy level above 98% concerning the legitimacy of the submitted tasks.