Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Secure Mobile Crowdsensing with Deep Learning   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.

2
The categories of this paper are: math
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int