Detection of hidden data attacks combined fog computing and trust evaluation method in sensor‐cloud system
Detection of hidden data attacks combined fog computing and trust evaluation method in sensor‐cloud system
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DOI：
10.1002/cpe.5109
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Guangxue Zhang;Tian Wang;Guojun Wang;Anfeng Liu;W. Jia
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文献类型：
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作者：
Guangxue Zhang;Tian Wang;Guojun Wang;Anfeng Liu;W. Jia
随着传感器云技术的普及，其安全问题越来越受到业界和学术界的关注。特别是，Sensor - Cloud底层网络由于其在计算、存储和分析方面的限制，非常容易受到内部攻击。现有的信任评估机制大多是从行为层面检测内部攻击问题。但是，在数据层面存在一些特殊的内部攻击，如隐藏数据攻击，这些攻击在行为层面是正常的，但会产生恶意数据，导致用户做出错误的决策。为了检测这种类型的攻击，我们设计了一种基于行为层信任评估机制的基于雾的检测系统（FDS）。本文定义了三种场景类型（冗余数据、参数曲线特征和数据验证），并给出了三种检测方案。实验结果表明，FDS在检测隐藏数据攻击方面具有一定的优势。
With the popularity of Sensor‐Cloud, its security issues get more attention from industry and academia. Especially, Sensor‐Cloud underlying network is very vulnerable to internal attacks due to its limitations in computing, storage, and analysis. Most existing trust evaluation mechanisms are proposed to detect internal attack issues from the behavior level. However, there are some special internal attacks in the data level such as hidden data attacks, which are normal in the behavior level but generate malicious data to lead user to make wrong decisions. To detect this type of attacks, we design a fog‐based detection system (FDS), which is based on the trust evaluation mechanism in the behavior level. In this paper, three types of scenes (the redundant data, the parameter curve characteristic, and the data validation) are defined, and three detection schemes are given. Some experiments are conducted, which manifest that FDS has certain advantages in detecting hidden data attacks.