Multimedia detection algorithm of malicious nodes in intelligent grid based on fuzzy logic

2019 ◽  
Vol 78 (17) ◽  
pp. 24011-24022 ◽  
Author(s):  
Mingming Gao ◽  
Yue Wu ◽  
Jingchang Nan ◽  
Shuyang Cui
Keyword(s):  
Fuzzy Logic ◽  
Detection Algorithm ◽  
Malicious Nodes ◽  
Grid Based

A Grid-Based Approach to Prolong Lifetime of WSNs Using Fuzzy Logic

2016 ◽  
pp. 11-22
Author(s):  
Ajai Kumar Mishra ◽  
Rakesh Kumar ◽  
Vimal Kumar ◽  
Jitendra Singh
Keyword(s):  
Fuzzy Logic ◽  
Grid Based

scholarly journals Abnormal User Detection Based on the Correlation Probabilistic Model

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaohui Yang ◽  
Ying Sun
Keyword(s):  
Probabilistic Model ◽  
User Behavior ◽  
Detection Algorithm ◽  
Malicious Nodes ◽  
Markov Logic ◽  
Correlation Degree ◽  
User Groups ◽  
Measurement Algorithm ◽  
And Behavior ◽  
User Security

As an important part of the new generation of information technology, the Internet of Things (IoT), which is developing rapidly, requires high user security. However, malicious nodes located in an IoT network can influence user security. Abnormal user detection and correlation probability analysis are fundamental and challenging problems. In this paper, the probabilistic model of the correlation between abnormal users (PMCAU) is proposed. First, the concept of user behavior correlation degree is proposed, which is defined as two parts: user attribute similarity degree and behavior interaction degree; the attribute similarity measurement algorithm and behavior correlation measurement algorithm are constructed, respectively, and the spontaneous and interactive behaviors of users were analyzed to determine the abnormal correlated users. Second, first-order logic grammar is used to express the before and after connection of user behavior and to deduce the probabilistic of occurrence of the correlation of behavior and determine the abnormal user groups. Experimental results show that, compared with the traditional anomaly detection algorithm and Markov logic network, this model can identify the users correlated with anomalies, make probabilistic inferences on the possible associations, and identify the potential abnormal user groups, thus achieving higher accuracy and predictability in the IoT.

Fuzzy logic-based jamming detection algorithm for cluster-based wireless sensor network

2018 ◽  
Vol 31 (10) ◽  
pp. e3567 ◽  
Author(s):  
K. P. Vijayakumar ◽  
P. Ganeshkumar ◽  
M. Anandaraj ◽  
K. Selvaraj ◽  
P. Sivakumar
Keyword(s):  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Jamming Detection
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