Deep Learning-Based Detection of Fake Task Injection in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a ubiquitous sensing paradigm where built-in sensors of smart mobile devices are empowered to acquire sensory data in lieu of dedicating large scale sensing infrastructures. One of the most crucial problems in mobile crowdsensing is the injection of fake sensing tasks to clog the energy, computing, storage and sensing resources of participating devices. In this paper, we present solutions that leverage deep networks to analyze the tasks submitted to MCS platforms. To this end, we model off-the-shelf deep learning models, namely Deep Autoencoder (Deep-AE), Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) in order to detect and filter out illegitimate tasks submitted to MCS campaigns. For the same purpose, we also utilize a Deep Multi-layer Perceptron (Deep-MLP) network instead of the well known Multi-layer Perceptron. Through numerical results on MCS data, we show that Deep-MLP outperforms its counterparts with 0.963 precision and 0.964 recall in the detection of fake sensing tasks.