BAN-Trust: An Attack-Resilient Malicious Node Detection Scheme for Body Area Networks

被引:0
作者
Li, Wenjia [1 ]
Zhu, Xianshu [2 ]
机构
[1] New York Inst Technol, Dept Comp Sci, New York, NY 10023 USA
[2] Dataminr Inc, New York, NY 10016 USA
来源
2016 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC) | 2016年
关键词
Body Area Network (BAN); security; trust; recommendation; Collaborative Filtering;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Body area networks (BAN) has recently emerged as an important enabling technology to support various telehealth applications. Because of its unique application domain, it is critical to ensure the secure and reliable gathering of patient's physiological signs. However, most of the existing security solutions for BANs focus on using encryption techniques to secure the data transmission or provide authentication. On the other hand, it is well understood that BANs are also extremely vulnerable to various malicious attacks, which have not attracted abundant research attention so far. In this paper, an attack-resilient malicious node detection scheme (BAN-Trust) is proposed for wireless body area networks that is able to detect and cope with malicious attacks in BANs. The effectiveness and efficiency of the proposed BAN-Trust scheme is validated through extensive experiments.
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页数:5
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