Dark Web Data Classification Using Neural Network. (28th March 2022)

• Record Type: Journal Article
• Authors: Rajawat, Anand Singh; Bedi, Pradeep; Goyal, S. B.; Kautish, Sandeep; Xihua, Zhang; Aljuaid, Hanan; Mohamed, Ali Wagdy
• Other Names: Khalil Ahmed Mostafa Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-03-28
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/8393318
• Languages: English
• ISSNs: 1687-5265

Abstract

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)-S 3 VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S 3 VM can improve the prediction.