Machine Learning-based Prevention of Battery-oriented Illegitimate Task Injection in Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) is a cloud-inspired and non-dedicated sensing paradigm to enable ubiquitous sensing via built-in sensors of personalized devices. Due to disparate participants and sensing tasks, MCS is vulnerable to threats initiated by malicious participants, which can either be a participant providing sensory data or an end user injecting a fake task aiming at resource (e.g. battery, sensor, etc.) clogging at the participating devices. This paper builds on machine learning-based detection of illegitimate tasks, and investigates the impact of machine learning-based prevention of battery-oriented illegitimate task injection in MCS campaigns. To this end, we introduce two different attack strategies, and test the impact of ML-based detection and elimination of fake tasks on task completion rate, as well as the overall battery drain of participating devices. Simulation results confirm that up to 14% battery power can be saved at the expense of a slight decrease in the completion rate of legitimate tasks.