A new security approach for public transport application against tag cloning with neural network-based pattern recognition

Düzenli, Gürsel

doi:10.1007/s00521-015-1837-8

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…In the actual dataset, the probability of each random event will be limited by some conditions in the process of occurrence. In order to introduce constraints to describe multidimensional random variables more comprehensively, for each event a i , a non‐negative real number ω i is assigned to form the weight of each event, forming entropy correlation, which can more profoundly describe the disorder degree of multidimensional random variables in the actual environment, and is more suitable for the constraints of outlier detection 14 . According to the above description, entropy correlation can be defined as:

H_{ω} false(X^{N} false) = - {false\sum}_{X^{1}}^{X^{N}} ω_{i} p false(a_{i} false) \log false(a_{i} false)

…”

Section: Construction Of Entropy Correlation Characteristic Matrixmentioning

confidence: 99%

Network security situation prediction based on combining associated entropy and deep recurrent neural network

Xin-cun

Wang

2020

Trans Emerging Tel Tech

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The existing network security situation prediction algorithm cannot deal with the subjective attitude deviation of multi-experts, and the traditional sequential machine learning model cannot deal with the problem of deviation accumulation in time period. In this paper, an algorithm of network security situation prediction combining entropy correlation and deep time series network model is proposed. First, the expert fuzzy evaluation index is constructed by trigonometric fuzzy function, and the modified weighted dempster-shafer (DS) evidence reasoning correction index is used, then the loss and possibility matrix features are created, and finally the information security is evaluated by deep time series network. The simulation experiments are carried out on MIT dataset. The experiments analyze whether the features can cope with multi-expert conflicts, and evaluate the accuracy, robustness, and time efficiency of the algorithm.The experimental results show that the algorithm proposed in this paper has stronger fuzzy evaluation ability, stronger ability to deal with conflict opinions among experts, more accurate prediction of network security situation in time sequence, and higher robustness, but the efficiency of the algorithm has been maintained.

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