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This study introduces a novel Fusion Neural Network (NN)-S³VM model to enhance Dark Web structural pattern mining. The model improves prediction accuracy for criminal network activities, addressing challenges in analyzing vast, multidimensional cybercrime data.

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Area of Science:

  • Cybersecurity
  • Data Mining
  • Network Analysis

Background:

  • Dark Web structural pattern mining faces challenges with redundant data, hindering the understanding of cybercrime like illegal trade and terrorism.
  • Vast amounts of multidimensional data complicate criminal behavior analysis and user profiling, leading to uncertain classification results and poor prediction of user actions.

Purpose of the Study:

  • To propose a novel model for improved Dark Web structural pattern mining and criminal network activity prediction.
  • To address the limitations of existing methods in handling multidimensional datasets and uncertain classification results.

Main Methods:

  • A Fusion Neural Network (NN)-S³VM model was developed for criminal network activity prediction.
  • The model leverages neural networks to enhance the analysis of Dark Web structural patterns.

Main Results:

  • The proposed NN-S³VM model demonstrates an improvement in predicting criminal network activities.
  • The fusion approach aims to overcome the adverse influence of feature mixes in multidimensional Dark Web data.

Conclusions:

  • The Fusion NN-S³VM model offers a promising approach to enhance Dark Web data analysis and cybercrime prediction.
  • Accurate prediction of user behavior and criminal activities can be improved through advanced data mining techniques.