English  |  正體中文  |  简体中文  |  Items with full text/Total items : 57969/91503 (63%)
Visitors : 13693834      Online Users : 39
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118491


    Title: The Sybil Attack in Participatory Sensing: Detection and Analysis
    Authors: Chang, Shih-Hao;Tseng, Kuo-Kun;Cheng, Shin-Ming
    Keywords: Cloud Computing;Trust Management;Cloud Service Provider;Adaptive Threshold;Reputation System
    Date: 2014-06
    Issue Date: 2020-04-09 12:10:35 (UTC+8)
    Abstract: Participatory sensing is a revolutionary paradigm in which volunteers collect and share information from their local environment using mobile phones. Nevertheless, one of the most important issues and misgiving about participatory sensing applications is security. Different from other participatory sensing application challenges who consider user privacy and data trustworthiness, we consider network trustworthiness problem namely Sybil attacks in participatory sensing. Sybil attacks is a particularly harmful attack against participatory sensing application, where Sybil attacks focus on creating multiple online user identities called Sybil identities and try to achieve malicious results through these identities. In this paper, we proposed a Hybrid Trust Management (HTM) framework for detecting and analyze Sybil attacks in participatory sensing network. Our HTM was proposed for performing Sybil attack characteristic check and trustworthiness management system to verify coverage nodes in the participatory sensing. To verify the proposed framework, we are currently developing the proposed scheme on OMNeT++ network simulator in multiple scenarios to achieve Sybil identities detection in our simulation environment.
    DOI: 10.1007/978-3-319-07776-5_30
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML52View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback