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基于窃电嫌疑程度的智能电网邻域网络恶意用户检测方法
Alternative TitleSmart power network neighborhood network malicious user detection method based on power stealing suspicion degree
梁炜; 夏小芳; 郑萌; 肖扬
Department工业控制网络与系统研究室
Rights Holder中国科学院沈阳自动化研究所
Patent Agent21002 沈阳科苑专利商标代理有限公司
Country中国
Subtype发明授权
Status有权
Abstract本发明涉及一种基于窃电嫌疑程度的智能电网邻域网络的恶意用户检测方法。包括用户嫌疑程度评估、二叉检测树建立以及恶意用户检测三个阶段。其中,在用户嫌疑程度评估阶段,分析用户窃电前科,并对用户的用电量的预测值及其上报值进行比较来分析用户的窃电可能性。基于用户的窃电嫌疑程度,建立一棵以用户为叶子节点的二叉检测树,并将其作为逻辑结构辅助查找恶意用户。在恶意用户检测阶段,采用自顶向下和深度优先搜索原则。子检测器只对该二叉检测树上的左孩子进行实际检测。本发明提出的检测器能够跳过二叉检测树上的绝大部分逻辑节点,从而快速、准确地定位智能电网邻居区域中的恶意用户。
Other AbstractThe invention relates to a smart power network neighborhood network malicious user detection method based on power stealing suspicion degree. The method comprises three phases: user suspicion degree evaluation, binary detection tree establishment and malicious user detection. In the user suspicion degree evaluation phase, user power stealing records are analyzed, and predicted values of power consumption of users are compared with reported values of the users, thereby analyzing power stealing possibility of the users. On the basis of the power stealing suspicion degree of the users, a binary detection tree taking the users as leaf nodes is established, and the binary detection tree is taken as a logic structure for assisting the search of the malicious users. In the malicious user detection phase, top-down and deep first search rules are employed. A sub-detector only carries out practical detection on left children of the binary detection tree. According to the detector provided by theinvention, the vast majority of logic nodes on the binary detection tree can be skipped, so the malicious users in a smart power network neighborhood area can be positioned rapidly and accurately.
PCT Attributes
Application Date2016-11-30
2018-06-05
Date Available2020-12-29
Application NumberCN201611077571.0
Open (Notice) NumberCN108123920B
Language中文
Contribution Rank1
Document Type专利
Identifierhttp://ir.sia.cn/handle/173321/28099
Collection工业控制网络与系统研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
梁炜,夏小芳,郑萌,等. 基于窃电嫌疑程度的智能电网邻域网络恶意用户检测方法[P]. 2018-06-05.
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