Author's Department
Mathematics & Actuarial Science Department
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https://doi.org/10.1155/2022/8393318
Document Type
Research Article
Publication Title
Computational Intelligence and Neuroscience
Publication Date
3-8-2022
doi
10.1155/2022/8393318
Abstract
There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction.
First Page
1
Last Page
11
Recommended Citation
APA Citation
Mohamed, A.
Rajawat, A.
Bedi, P.
&
Goyal, S.
(2022). Dark Web Data Classification Using Neural Network. Computational Intelligence and Neuroscience, 2022, 1–11.
10.1155/2022/8393318
https://fount.aucegypt.edu/faculty_journal_articles/4760
MLA Citation
Mohamed, Ali Wagdy, et al.
"Dark Web Data Classification Using Neural Network." Computational Intelligence and Neuroscience, vol. 2022, 2022, pp. 1–11.
https://fount.aucegypt.edu/faculty_journal_articles/4760