Monitoring Unethical Activities on the Dark Web Through AI

The dark web is a notorious segment of the internet that facilitates various illicit activities, including drug trafficking, human trafficking, and cybercrime. Traditional monitoring techniques often fall short in identifying and combating these activities. This paper proposes an AI-based framework leveraging advanced machine learning algorithms, natural language processing (NLP), and data analytics to detect and monitor unethical activities on the dark web. Additionally, the paper addresses the ethical and legal implications of such monitoring practices and suggests ways to mitigate associated challenges.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-12
DOI
https://doi.org/10.5281/zenodo.13829018
Primary Topic
Plant responses to water stress
Type
article
Field-Weighted Citation Impact
0.00
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article

Monitoring Unethical Activities on the Dark Web Through AI

Zenodo (CERN European Organization for Nuclear Research)
Plant responses to water stress
article

Monitoring Unethical Activities on the Dark Web Through AI

article en

Abstract

The dark web is a notorious segment of the internet that facilitates various illicit activities, including drug trafficking, human trafficking, and cybercrime. Traditional monitoring techniques often fall short in identifying and combating these activities. This paper proposes an AI-based framework leveraging advanced machine learning algorithms, natural language processing (NLP), and data analytics to detect and monitor unethical activities on the dark web. Additionally, the paper addresses the ethical and legal implications of such monitoring practices and suggests ways to mitigate associated challenges.

Zenodo (CERN European Organization for Nuclear Research)
Gender equality
Openalex Percentile: Top 77%
Plant responses to water stress
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