Secure mobile crowdsensing based on deep learning
Secure mobile crowdsensing based on deep learning is a scholarly work, published in 2018 in ''China Communications''. The main subjects of the publication include deep learning, mobile device, computer security, artificial intelligence, reinforcement learning, differential privacy, crowdsourcing, computer network, indoor positioning system, authentication, autoencoder, spoofing attack, and computer science. The authors investigate secure mobile crowdsensing and present ways to use deep learning (DL) methods, such as stacked autoencoder, deep neural networks, convolutional neural networks, and deep reinforcement learning, to improve approaches to MCS security, including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS.