Secure Mobile Crowdsensing with Deep Learning

TL;DR

This paper explores how deep learning techniques can enhance security in mobile crowdsensing systems for IoT, addressing threats like spoofing, jamming, and fake sensing through advanced neural network models.

Contribution

It introduces the application of deep learning models such as SAE, DNN, and CNN to improve security measures in mobile crowdsensing, including authentication and intrusion detection.

Findings

DL-based security approaches outperform traditional schemes

Deep learning models effectively detect spoofing and jamming attacks

Challenges for practical implementation are discussed

Abstract

In order to stimulate secure sensing for Internet of Things (IoT) applications such as healthcare and traffic monitoring, mobile crowdsensing (MCS) systems have to address security threats, such as jamming, spoofing and faked sensing attacks, during both the sensing and the information exchange processes in large-scale dynamic and heterogenous networks. In this article, we investigate secure mobile crowdsensing and present how to use deep learning (DL) methods such as stacked autoencoder (SAE), deep neural network (DNN), and convolutional neural network (CNN) to improve the MCS security approaches including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DL-based approaches compared with traditional security schemes and identify the challenges that need to be addressed to implement them in practical MCS systems.