北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (3): 37-42.doi: 10.13190/j.jbupt.2018-114

• 论文 • 上一篇    下一篇

基于任务转移概率的感知节点异常运行状态检测方法

马峻岩, 张特, 王瑾   

  1. 长安大学 信息工程学院, 西安 710064
  • 收稿日期:2018-06-04 出版日期:2019-06-28 发布日期:2019-06-20
  • 作者简介:马峻岩(1982-),男,副教授,E-mail:majy@chd.edu.cn.
  • 基金资助:
    国家自然科学基金项目(61402050,61303041);国家重点研发计划项目(2017YFC0804806,2018YFB1600802)

Task Transition Probability Based Anomaly Detection Method for Sensor Nodes

MA Jun-yan, ZHANG Te, WANG Jin   

  1. School of Information Engineering, Chang'an University, Xi'an 710064, China
  • Received:2018-06-04 Online:2019-06-28 Published:2019-06-20

摘要: 针对无线传感器网络中感知节点异常状态检测困难问题,提出了一种基于感知节点任务转移概率的节点状态特征描述方式,利用该特征判断感知节点的运行状态,实现节点异常检测.基于任务转移概率的异常检测方法(T2PAD),根据感知节点运行任务的一步转移概率特征,对节点的运行状态进行分析,通过对转移概率向量相似性进行异常检测,识别出导致异常的任务,缩小并定位异常范围,为修正异常提供依据.传感器网络开源代码库中的缺陷实例验证了T2PAD对于异常检测的有效性.

关键词: 无线传感器网络, 感知节点异常, 转移概率矩阵, 节点状态特征

Abstract: The anomaly detection of sensor nodes is a great challenge to wireless sensor networks. A feature based on task transition probability is therefore proposed to model running states of sensor nodes, and the feature can be further used for anomaly detection. Task transition probability based anomaly detection (T2PAD) analyzes states of sensor nodes based on the one-step transition probability of running tasks within the nodes, and then performs anomaly detection by comparing similarities between transition probability vectors. T2PAD can identify tasks that caused the anomaly and narrow down the scope of problematic code, which provides clues to deal with the anomaly. Case studies on defects from a sensor network open source project are carried out to verify the effectiveness of T2PAD.

Key words: wireless sensor network, anomaly of sensor node, transition probability matrix, node state feature

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