Mobile crowd sensing (MCS) arises as a new sensing paradigm, which leverages citizens for large-scale sensing by various mobile devices to efficiently collect and share local information. Unlike other MCS application challenges that consider user privacy and data trustworthiness, this study focuses on the network trustworthiness problem, namely Sybil attacks in MCS network. The Sybil attack in computer security is a type of security attacks, which illegally forge multiple identities in peer-to-peer networks, namely Sybil identities. These Sybil identities will falsify multiple identities to negatively influence to reduce the effectiveness of sensing data in this MCS network or degrading entire network performance. To cope with this problem, a cloud based trust management scheme (CbTMS) was proposed to detect Sybil attacks in the MCS network. The CbTMS was proffered for performing active and passive checking scheme, in addition to the mobile PCS trustworthiness management and include a decision tree algorithm, to verify the covered nodes in the MCS network. Simulation studies shows that our CbTMS can efficiently detect the malicious Sybil nodes in the network and reduce 6.87 Wh compared with in a malicious Sybil node attack mode.
Relation:
Mobile Information Systems 2016, 6506341(10 pages)
