Cybercrime Prevention Methodology Using Web Scraping and Natural Language Processing for the Detection of Data Leaks in the Dark Web
Abstract This paper sets out a methodology for the capture and processing of data that has been leaked and made available for sale on the Dark Web, a part of the internet that is known for its high prevalence of criminal content. A significant challenge in the collection and processing of critical data sold on the Dark Web is the volatility of the content and its lack of structure. To address these challenges, a methodology employing Web Scraping and Natural Language Processing (NLP) techniques is proposed for the detection of sensitive data published on the Internet black market. The aim is to prevent its use in cases of extortion, identity theft, disclosure of secrets and other types of cybercrime.The developed software represents a significant advancement in terms of social welfare, as it facilitates the automated monitoring and interpretation of data leaked on the Dark Web (e.g. credit card details, personal identification numbers, etc.), thereby ensuring the privacy of the individuals and organisations concerned. Moreover, by offering a more efficient and precise approach to address these threats, the adoption of the architecture proposed in this work contributes to the creation of a safer online environment for all users.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Al Nabki, M. W., Fidalgo, E., Alegre, E. y De Paz, I. (2017). Classifying illegal activities on Tor network based on web textual contents. En Conference of the European Chapter of the Association for Computational Linguistics (pp. 35-43). https://doi.org/10.18653/V1/E17-1004
Alneyadi, S., Sithirasenan, E. y Muthukkumarasamy, V. (2016). A survey on data leakage prevention systems. Journal of Network and Computer Applications, 62, 137-152. https://doi.org/10.1016/j.jnca.2016.01.008
Ciberprotection-magazine. (2021). Here is how hackers attacked the Oldsmar water supply – and how a catastrophe was prevented. Recuperado el 12-02-2021 de https://cyberprotection-magazine.com/here-is-how-hackers-attacked-the-oldsmar-water-supply-and-how-a-catastrophe-was-prevented
Código Penal [CP]. Art. 197. 24 noviembre de 1995 (España).
Código Penal [CP]. Art. 298. 24 de noviembre de 1995 (España).
Connolly, K., Klempay, A., McCann, M. y Brenner, P. (2023). Dark web marketplaces: Data for collaborative threat intelligence. Digital Threats: Research and Practice, 4(4), 1-12. https://doi.org/10.1145/3615666
Ewala IT Services. (2023). Karonte Fuga de Datos. https://www.karont3.tech/img/cms/Karont3%20Tendencias%202023_12%201.pdf
Gallo-Serpillo, F. y Valls-Prieto, J. (2024). Analysis of CSEM offenders on the dark web using honeypots to geolocate IP addresses from Spain. Computers in Human Behavior, 154(108137), 108137. https://doi.org/10.1016/j.chb.2024.108137
Gede, I., Rahayuda, S., Putu, N. y Santiari, L. (2017). Crawling and Cluster Hidden Web Using Crawler Framework and Fuzzy-KNN. Conference: 2017 5th International Conference on Cyber and IT Service Management (CITSM).
Guo, Z., Jin, R., Liu, C., Huang, Y., Shi, D., Supryadi, Yu, L., Liu, Y., Li, J., Xiong, B. y Xiong, D. (2023). Evaluating large language models: A comprehensive survey. arXiv [cs.CL]. http://arxiv.org/abs/2310.19736
Karabey Aksakalli, I., Çelik, T., Can, A. B. y Teki̇nerdoğan, B. (2021). Deployment and communication patterns in microservice architectures: A systematic literature review. The Journal of Systems and Software, 180(111014), 111014. https://doi.org/10.1016/j.jss.2021.111014
Lacey, D. y Salmon, P. M. (2015). It’s dark in there: Using systems analysis to investigate trust and engagement in dark web forums. En Lecture Notes in Computer Science (pp. 117-128). Springer International Publishing.
Lin, F., Liu, Y., Ebrahimi, M., Ahmad-Post, Z., Hu, J. L., Xin, J., Samtani, S., Li, W. y Chen, H. (2020). Linking personally identifiable information from the dark web to the surface web: A deep entity resolution approach. En 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 488-495).
Mitchell, R. (2015). Web scraping with Python: Collecting more data from the modern web. O’Reilly Media.
Naskali, J., Rantanen, M., Rottenkolber, M. y Kimppa, K. K. (2024). Smart Ethics in the Digital World. Proceedings of the ETHICOMP 2024. Smart Ethics in the Digital World Logroño.
Nayak, S. K. y Ojha, A. C. (2020). Data leakage detection and prevention: Review and research directions. En Machine Learning and Information Processing: Proceedings of ICMLIP 2019 (pp. 203-212).
Neto, N. N., Madnick, S., Paula, A. M. G. D. y Borges, N. M. (2021). Developing a global data breach database and the challenges encountered. ACM Journal of Data and Information Quality, 13(1), 1-33. https://doi.org/10.1145/3439873
Ojoawo, A. O., Fagbolu, O. O., Olaniyan, A. S. y Sonubi, T. A. (s. f.). Data leak protection using text mining and social network analysis. Ijerd.com. Recuperado el 2-3-2025 de: http://www.ijerd.com/paper/vol10-issue12/Version_3/B10121422.pdf
Pimenta Rodrigues, G. A., Marques Serrano, A. L., Lopes Espiñeira Lemos, A. N., Canedo, E. D., Mendonça, F. L. L. de, De Oliveira Albuquerque, R., Sandoval Orozco, A. L. y García Villalba, L. J. (2024). Understanding data breach from a global perspective: Incident visualization and data protection law review. Data, 9(2), 27. https://doi.org/10.3390/data9020027
Saleem, J., Islam, R. y Kabir, M. A. (2022). The anonymity of the dark web: A survey. IEEE Access: Practical Innovations, Open Solutions, 10, 33628-33660. https://doi.org/10.1109/access.2022.3161547
Zhang, Z., He, B. y Chang, K. C.-C. (2004). Understanding Web query interfaces: Best-effort parsing with hidden syntax. En Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (pp. 107-118).
Alneyadi, S., Sithirasenan, E. y Muthukkumarasamy, V. (2016). A survey on data leakage prevention systems. Journal of Network and Computer Applications, 62, 137-152. https://doi.org/10.1016/j.jnca.2016.01.008
Ciberprotection-magazine. (2021). Here is how hackers attacked the Oldsmar water supply – and how a catastrophe was prevented. Recuperado el 12-02-2021 de https://cyberprotection-magazine.com/here-is-how-hackers-attacked-the-oldsmar-water-supply-and-how-a-catastrophe-was-prevented
Código Penal [CP]. Art. 197. 24 noviembre de 1995 (España).
Código Penal [CP]. Art. 298. 24 de noviembre de 1995 (España).
Connolly, K., Klempay, A., McCann, M. y Brenner, P. (2023). Dark web marketplaces: Data for collaborative threat intelligence. Digital Threats: Research and Practice, 4(4), 1-12. https://doi.org/10.1145/3615666
Ewala IT Services. (2023). Karonte Fuga de Datos. https://www.karont3.tech/img/cms/Karont3%20Tendencias%202023_12%201.pdf
Gallo-Serpillo, F. y Valls-Prieto, J. (2024). Analysis of CSEM offenders on the dark web using honeypots to geolocate IP addresses from Spain. Computers in Human Behavior, 154(108137), 108137. https://doi.org/10.1016/j.chb.2024.108137
Gede, I., Rahayuda, S., Putu, N. y Santiari, L. (2017). Crawling and Cluster Hidden Web Using Crawler Framework and Fuzzy-KNN. Conference: 2017 5th International Conference on Cyber and IT Service Management (CITSM).
Guo, Z., Jin, R., Liu, C., Huang, Y., Shi, D., Supryadi, Yu, L., Liu, Y., Li, J., Xiong, B. y Xiong, D. (2023). Evaluating large language models: A comprehensive survey. arXiv [cs.CL]. http://arxiv.org/abs/2310.19736
Karabey Aksakalli, I., Çelik, T., Can, A. B. y Teki̇nerdoğan, B. (2021). Deployment and communication patterns in microservice architectures: A systematic literature review. The Journal of Systems and Software, 180(111014), 111014. https://doi.org/10.1016/j.jss.2021.111014
Lacey, D. y Salmon, P. M. (2015). It’s dark in there: Using systems analysis to investigate trust and engagement in dark web forums. En Lecture Notes in Computer Science (pp. 117-128). Springer International Publishing.
Lin, F., Liu, Y., Ebrahimi, M., Ahmad-Post, Z., Hu, J. L., Xin, J., Samtani, S., Li, W. y Chen, H. (2020). Linking personally identifiable information from the dark web to the surface web: A deep entity resolution approach. En 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 488-495).
Mitchell, R. (2015). Web scraping with Python: Collecting more data from the modern web. O’Reilly Media.
Naskali, J., Rantanen, M., Rottenkolber, M. y Kimppa, K. K. (2024). Smart Ethics in the Digital World. Proceedings of the ETHICOMP 2024. Smart Ethics in the Digital World Logroño.
Nayak, S. K. y Ojha, A. C. (2020). Data leakage detection and prevention: Review and research directions. En Machine Learning and Information Processing: Proceedings of ICMLIP 2019 (pp. 203-212).
Neto, N. N., Madnick, S., Paula, A. M. G. D. y Borges, N. M. (2021). Developing a global data breach database and the challenges encountered. ACM Journal of Data and Information Quality, 13(1), 1-33. https://doi.org/10.1145/3439873
Ojoawo, A. O., Fagbolu, O. O., Olaniyan, A. S. y Sonubi, T. A. (s. f.). Data leak protection using text mining and social network analysis. Ijerd.com. Recuperado el 2-3-2025 de: http://www.ijerd.com/paper/vol10-issue12/Version_3/B10121422.pdf
Pimenta Rodrigues, G. A., Marques Serrano, A. L., Lopes Espiñeira Lemos, A. N., Canedo, E. D., Mendonça, F. L. L. de, De Oliveira Albuquerque, R., Sandoval Orozco, A. L. y García Villalba, L. J. (2024). Understanding data breach from a global perspective: Incident visualization and data protection law review. Data, 9(2), 27. https://doi.org/10.3390/data9020027
Saleem, J., Islam, R. y Kabir, M. A. (2022). The anonymity of the dark web: A survey. IEEE Access: Practical Innovations, Open Solutions, 10, 33628-33660. https://doi.org/10.1109/access.2022.3161547
Zhang, Z., He, B. y Chang, K. C.-C. (2004). Understanding Web query interfaces: Best-effort parsing with hidden syntax. En Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (pp. 107-118).
Rico Pachón, N., Gallo-Serpillo, F., & Barroso, R. (2025). Cybercrime Prevention Methodology Using Web Scraping and Natural Language Processing for the Detection of Data Leaks in the Dark Web. Ciencia Policial, 184, 87–113. https://doi.org/10.14201/cp.32279
Similar Articles
- María Luisa García Torres, Cybersecurity vs Cybercrime: Procedural Obstacles in the Prosecution of Organized Cybercrime. Proposals for a More Effective Repression of Cybercrime , Ciencia Policial: Vol. 182 (2024)
- José Manuel Rodríguez-Jiménez, Management of Diffused Information in Databases Using Similarity Measures , Ciencia Policial: Vol. 185 (2025)
- María Luisa García Torres, Predictive Artificial Intelligence in the Service of Crime Prevention, Investigation, and Criminal Proceedings , Ciencia Policial: Vol. 183 (2024)
- José Manuel Rodríguez Jiménez, Miguel Ángel Canorea Ruiz, Alejandro Plaza Quesada, Detection of Tampering of Engines Located in Motorbikes , Ciencia Policial: Vol. 183 (2024)
- Daniel González Uriel, Some Difficulties in the Detection and Investigation of Economic Cybercrimes , Ciencia Policial: Vol. 183 (2024)
- Beatriz López Pérez, Analysis of Conspiracy Narratives and Migration Disinformation on Telegram Using SOCMINT Techniques , Ciencia Policial: Vol. 185 (2025)
- Desiderio José Ordoño Ballesteros, The Application of Bioforensic Sciences to Bioterrorism and Biocrime Research , Ciencia Policial: Vol. 182 (2024)
- Adriano J. Alfonso Rodríguez, Interception of Telephone Communications, Security(s) and Procedural Safeguards , Ciencia Policial: Vol. 182 (2024)
- Raquel Amezcua de Miguel, Analysis and Implementation of Strategies to Prevent or Mitigate Cognitive ‘Contamination’ in the Collection, Analysis and Interpretation of Forensic-Scientific Evidence in Criminal Proceedings , Ciencia Policial: Vol. 183 (2024)
- Víctor Manuel Sánchez Hernández, Evolution and Current Situation of Jihadist Terrorism: Dynamics, Challenges and Future Prospects , Ciencia Policial: Vol. 184 (2025)
You may also start an advanced similarity search for this article.
+
−