Document Type : Articles


1 Research Fellow/ Lecturer, School of Management, IT and Governance, University of KwaZulu-Natal, Durban, South Africa

2 Research Fellow/ Lecturer, School of Management, IT and Governance, University of KwaZulu-Natal, Durban, South Africa.


The dark web is considered an expansion of the deep web, intentionally hidden from the surface web. It can only be accessed with a particular group of browsers that allow the user to stay anonymous while navigating the dark web. With the untraceable hidden layer of the Internet and the anonymity of the users associated with the dark web, several impressive cybercrimes have been reported. This paper aims to examine the impact of the dark web on society. The article systematically reviews relevant academic literature and books to understand how the dark web works and its societal effects. The study has found that the dark web is an enabler of several cybercrimes. Moreover, while governments and regulatory authorities have introduced strategic detection techniques on the dark web, cybercriminals are adaptive towards the strategies and, given time, will usually find ways to bypass such detection techniques. It is recommended that the regulatory authorities and cyber threat intelligence periodically review the detection techniques for effective monitoring. Furthermore, security agencies or forensic analysts should ensure that they are updated with the latest scientific knowledge on the safe management of the dark web by undertaking more training in cyber security. There is also a need for further research to focus on awareness campaigns about the dangers of the dark web.


Ablon, L., Libicki, M. C. & Golay, A. A. (2014). Markets for cybercrime tools and stolen data: Hackers' bazaar. Rand Corporation. Retrieved from
Aceto, G. & Pescapé, A. (2015). Internet censorship detection: A survey. Computer Networks, 83, 381-421.
Albrecht, C., Duffin, K. M., Hawkins, S. & Rocha, V. M. M. (2019). The use of cryptocurrencies in the money laundering process. Journal of Money Laundering Control, 22(2), 210-216.
Aldridge, J., Decary-Hetu, D. & EMCDDA, U. (Ed.) (2015). Cryptomarkets and the future of illicit drug markets. In The Internet and Drug markets (pp. 23-32). (Insights; Vol. 21). Publications Office of the European Union.
Basheer, R. & Alkhatib, B. (2021). Threats from the dark: A review over dark web investigation research for cyber threat intelligence. Journal of Computer Networks and Communications.
Bates, R. A. (2016). Tracking lone wolf terrorists. The Journal of Public and Professional Sociology, 8(1), 6.
Bertola, F. (2020). Drug trafficking on Darkmarkets: How cryptomarkets are changing drug global trade and the role of organized crime. American Journal of Qualitative Research, 4(2), 27-34.
Besenyő, J. & Gulyas, A. (2021). The effect of the dark web on the security. Journal of Security & Sustainability Issues, 11(1), 103-121.
Bhakiyalakshmi, K., Vidhyalakshmi, G., Kumaresan, A. & Vijayakumar, K. (2017). Network traffic classification using correlation information. Advances in Natural and Applied Sciences, 11(6 SI), 76-82.
Biswas, R., Fidalgo, E., & Alegre, E. (2017, December). Recognition of service domains on TOR dark net using perceptual hashing and image classification techniques. In 8th International Conference on Imaging for Crime Detection and Prevention (ICDP 2017) (pp. 7-12). IET.
Broadhurst, R., Woodford-Smith, H., Maxim, D., Sabol, B., Orlando, S., Chapman-Schmidt, B. & Alazab, M. (2017). Cyber terrorism: research review: research report of the Australian national university cybercrime observatory for the Korean institute of criminology.
Broséus, J., Rhumorbarbe, D., Mireault, C., Ouellette, V., Crispino, F. & Décary-Hétu, D. (2016). Studying illicit drug trafficking on Darknet markets: structure and organization from a Canadian perspective. Forensic Science International, 264, 7-14.
Bryans, D. (2014). Bitcoin and money laundering: mining for an effective solution. Indian Legal Journals, 89, 441. Retrieved from
Brynielsson, J., Horndahl, A., Johansson, F., Kaati, L., Mårtenson, C. & Svenson, P. (2013). Harvesting and analysis of weak signals for detecting lone wolf terrorists. Security Informatics, 2, 11.
Bu, Z., Xia, Z. & Wang, J. (2013). A sock puppet detection algorithm on virtual spaces. Knowledge-Based Systems, 37, 366-377.
Burbano, D. & Hernandez-Alvarez, M. (2017, October). Identifying human trafficking patterns online. In 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.
Chaudhari, R. R. & Patil, S. P. (2017). Intrusion detection system: classification, techniques and datasets to implement. International Research Journal of Engineering and Technology (IRJET), 4(2), 1860-1866. Retrieved from
Chawki, M. (2022). The Dark Web and the future of illicit drug markets. Journal of Transportation Security, 15, 173-191.
Chertoff, M. & Simon, T. (2015). The impact of the dark web on internet goverannce and cyber security. the Centre for International Governance Innovation and Chatham House. Retrieved from
Christin, N. (2013, May). Traveling the Silk Road: A measurement analysis of a large anonymous online marketplace. In Proceedings of the 22nd international conference on World Wide Web (pp. 213-224).
Cole, R., Latif, S. & Chowdhury, M. M. (2021, October). Dark web: A facilitator of crime. In 2021 international conference on electrical, computer, communications and mechatronics engineering (iceccme) (pp. 1-6). IEEE.
Copeland, C., Wallin, M. & Holt, T. J. (2020). Assessing the practices and products of Darkweb Firearm vendors. Deviant Behavior, 41(8), 949-968.
Dalins, J., Wilson, C. & Carman, M. (2018). Criminal motivation on the dark web: A categorization model for law enforcement. Digital Investigation, 24, 62-71.
DiPiero, C. (2017). Deciphering cryptocurrency: Shining a light on the deep dark web. University of Illinois Law Review, 3, 1267-1299.
Duxbury, S. W. & Haynie, D. L. (2018). Building them up, breaking them down: Topology, vendor selection patterns, and a digital drug market’s robustness to disruption. Social Networks, 52, 238-250.
Ehney, R. & Shorter, J. D. (2016). Deep web, dark web, invisible web and the post ISIS world. Issues in Information Systems, 17(4), 36-41. 
Ehrenfeld, J. M. (2017). Wannacry, cybersecurity and health information technology: A time to act. Journal of Medical Systems, 41(7), 104.
Eichler, G. M. & Schwarz, E. J. (2019). What sustainable development goals do social innovations address? A systematic review and content analysis of social innovation literature. Sustainability, 11(2), 522.
Elmellas, J. (2016). Knowledge is power: the evolution of threat intelligence. Computer Fraud & Security, 7, 5-9.
Fan, W., Du, Z., Fernández, D. & Villagra, V. A. (2017). Enabling an anatomic view to investigate honeypot systems: A survey. IEEE Systems Journal, 12(4), 3906-3919.
Ghappour, A. (2017). Searching places unknown: Law enforcement jurisdiction on the dark web. Stanford Law Review., 69, 1075-1136. Retrieved from
Gokhale, C. & Olugbara, O. O. (2020). Dark web traffic analysis of cybersecurity threats through South African Internet protocol address space. SN Computer Science, 1, 273.
Gupta, A., Maynard, S. B. & Ahmad, A. (2019). The dark web phenomenon: A review and research agenda. In Australasian Conference on Information Systems. Perth, WA.  Retrieved from
Hammonds, J. (2015). An inquiry into privacy concerns: Memex the deep Web and sex trafficking. Retrieved from
Hayes, D. R., Cappa, F. & Cardon, J. (2018). A framework for more effective dark web marketplace investigations. Information, 9(8), 186.
He, S., He, Y. & Li, M. (2019, March). Classification of illegal activities on the dark web. In Proceedings of the 2nd International Conference on Information Science and Systems (pp. 73-78).
Heinl, M. P., Yu, B. & Wijesekera, D. (2019). A Framework to Reveal Clandestine Organ Trafficking in the Dark Web and Beyond. Journal of Digital Forensics, Security and Law, 14(1), 2.
Holland, B. J. (2020). Transnational cybercrime: The dark web. Encyclopedia of Criminal Activities and the Deep Web, 108-128.
Jardine, E. (2018). Privacy, censorship, data breaches and Internet freedom: The drivers of support and opposition to Dark Web technologies. New Media & Society, 20(8), 2824-2843.
Jin, Y.W., Jang, E., Lee, Y., Shin, S. & Chung, J. (2022). Shedding New Light on the Language of the Dark Web. North American Chapter of the Association for Computational Linguistics. Retrieved from
Kaur, S. & Randhawa, S. (2020). Dark web: A web of crimes. Wireless Personal Communications, 112, 2131-2158.
Kavallieros, D., Myttas, D., Kermitsis, E., Lissaris, E., Giataganas, G. & Darra, E. (2021). Understanding the dark web. In Dark Web Investigation (pp. 3-26). Springer.
Kheshaifaty, N. & Gutub, A. (2020). Preventing multiple accessing attacks via efficient integration of captcha crypto hash functions. IJCSNS International Journal of Computer Science and Network Security, 20(9), 16-28. Retrieved from
Koh, B. (2011). User profiling in online marketplaces and security. The University of Texas at Dallas.
Koniaris, I., Papadimitriou, G., Nicopolitidis, P. & Obaidat, M. (2014, June). Honeypots deployment for the analysis and visualization of malware activity and malicious connections. In 2014 IEEE international conference on communications (ICC) (pp. 1819-1824). IEEE.
Kumar, S., Cheng, J., Leskovec, J. & Subrahmanian, V. S. (2017, April). An army of me: Sockpuppets in online discussion communities. In Proceedings of the 26th international conference on world wide web (pp. 857-866).
Lee, S., Yoon, C., Kang, H., Kim, Y., Kim, Y., Han, D., Son, S. & Shin, S. (2019, February). Cybercriminal minds: an investigative study of cryptocurrency abuses in the dark web. In 26TH Annual Network and Distributed System Security Symposium (NDSS 2019) (pp. 1-15). Internet Society.
Ling, Z., Luo, J., Yu, W., Fu, X., Xuan, D. & Jia, W. (2012). A new cell-counting-based attack against Tor. IEEE/ACM Transactions On Networking, 20(4), 1245-1261.
Liu, D., Wu, Q., Han, W. & Zhou, B. (2016). Sockpuppet gang detection on social media sites. Frontiers of Computer Science, 10, 124-135.
Mador, Z. (2021). Keep the dark web close and your cyber security tighter. Computer Fraud & Security, 1, 6-8.
Maity, S. K., Chakraborty, A., Goyal, P. & Mukherjee, A. (2017, February). Detection of sockpuppets in social media. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 243-246).
Mallett, R., Hagen-Zanker, J., Slater, R. & Duvendack, M. (2012). The benefits and challenges of using systematic reviews in international development research. Journal of Development Effectiveness, 4(3), 445-455.
Mattmann, C. A. (2015, December). Search of the deep and dark web via darpa memex. In AGU Fall Meeting Abstracts (Vol. 2015, pp. IN33A-1795).
Me, G. & Pesticcio, L. (2018). Tor black markets: economics, characterization and investigation technique. In Cyber Criminology (pp. 119-140). Springer.
Melsky, M. (2018). The Dark Corners of the Lindbergh Kidnapping. Vol. 2. iUniverse.
Mishra, P., Pilli, E. S., Varadharajan, V. & Tupakula, U. (2017). Intrusion detection techniques in cloud environment: A survey. Journal of Network and Computer Applications, 77, 18-47.
Moore, C. (2016, August). Detecting ransomware with honeypot techniques. In 2016 Cybersecurity and Cyberforensics Conference (CCC) (pp. 77-81). IEEE.
Nazah, S., Huda, S., Abawajy, J. & Hassan, M. M. (2020). Evolution of dark web threat analysis and detection: A systematic approach. IEEE Access, 8, 171796 -171819.
Odendaal, R., Hattingh, M. & Eybers, S. (2019, September). The good, the bad and the ugly of the dark web: Perceptions of South African students and parents. In Proceedings of the South African Institute of Computer Scientists and Information Technologists  (pp. 1-9).
Revell, T. (2017). US guns sold in Europe via dark web. New Scientist, 3136. 
Rhumorbarbe, D., Werner, D., Gilliéron, Q., Staehli, L., Broséus, J. & Rossy, Q. (2018). Characterizing the online weapons trafficking on cryptomarkets. Forensic Science International, 283, 16-20.
Samtani, S., Chinn, R., Chen, H. & Nunamaker Jr, J. F. (2017). Exploring emerging hacker assets and key hackers for proactive cyber threat intelligence. Journal of Management Information Systems, 34(4), 1023-1053.
Scanlon, J. R. & Gerber, M. S. (2014). Automatic detection of cyber-recruitment by violent extremists. Security Informatics, 3, 5.
Schäfer, M., Fuchs, M., Strohmeier, M., Engel, M., Liechti, M. & Lenders, V. (2019, May). BlackWidow: Monitoring the dark web for cyber security information. In 2019 11th International Conference on Cyber Conflict (CyCon) (Vol. 900, pp. 1-21). IEEE.
Scholz, R. W. (2016). Sustainable digital environments: What major challenges is humankind facing? Sustainability, 8(8), 726.
Singh, R., Amritha, P. P. & Sethumadhavan, M. (2022, April). Scoring Scheme to Determine the Sensitive Information Level in Surface Web and Dark Web. In International Conference on Advances in Computing and Data Sciences (pp. 157-167). Cham: Springer International Publishing.
Sönmez, E. & Seçkin Codal, K. (2022). Terrorism in cyberspace: A critical review of dark web studies under the terrorism landscape. Sakarya University Journal of Computer and Information Sciences, 5(1), 1-21. saucis.05.01. 950746
Soska, K. & Christin, N. (2015). Measuring the longitudinal evolution of the online anonymous marketplace ecosystem. In 24th USENIX security symposium (USENIX security 15) (pp. 33-48). Retrieved from
Spitters, M., Klaver, F., Koot, G. & Van Staalduinen, M. (2015, September). Authorship analysis on dark marketplace forums. In 2015 European Intelligence and Security Informatics Conference (pp. 1-8). IEEE.
Stapic, Z., López, E. G., Cabot, A. G., de Marcos Ortega, L. & Strahonja, V. (2012). Performing systematic literature review in software engineering. In Central European Conference on Information and Intelligent Systems (p. 441-447). Faculty of Organization and Informatics Varazdin.
Surette, R. (2015). Performance crime and justice. Current Issues in Criminal Justice, 27(2), 195-216.
Taleby Ahvanooey, M., Zhu, M. X., Mazurczyk, W., Kilger, M. & Choo, K. K. R. (2021, December). Do dark web and cryptocurrencies empower cybercriminals?. In International Conference on Digital Forensics and Cyber Crime (pp. 277-293). Cham: Springer International Publishing.
Topor, L. (2019). Dark Hatred: Antisemitism on the Dark Web. Journal of Contemporary Antisemitism, 2(2), 25-42.
Upulie, H. & Prasanga, P. (2021). Dark Web, Its Impact on the Internet and the Society: A Review.  
Van Wegberg, R., Oerlemans, J.-J. & van Deventer, O. (2018). Bitcoin money laundering: mixed results? An explorative study on money laundering of cybercrime proceeds using bitcoin. Journal of Financial Crime, 25(2), 419-435.
Volety, T., Saini, S., McGhin, T., Liu, C. Z. & Choo, K.-K. R. (2019). Cracking bitcoin wallets: I want what you have in the wallets. Future Generation Computer Systems, 91, 136-143.
Vyas, P., Vyas, G., Chauhan, A., Rawat, R., Telang, S. & Gottumukkala, M. (2022). Anonymous Trading on the Dark Online Marketplace: An Exploratory Study. In Using Computational Intelligence for the Dark Web and Illicit Behavior Detection (pp. 272-289). IGI Global.
Webb, J., Ahmad, A., Maynard, S. B. & Shanks, G. (2014). A situation awareness model for information security risk management. Computers & Security, 44, 1-15.
Weimann, G. (2016). Going dark: Terrorism on the dark web. Studies in Conflict & Terrorism, 39(3), 195-206.
Zhang, J., Xiang, Y., Wang, Y., Zhou, W., Xiang, Y. & Guan, Y. (2012). Network traffic classification using correlation information. IEEE Transactions on Parallel and Distributed systems, 24(1), 104-117.
Zhang, X. & Chow, K. (2020). A framework for dark Web threat intelligence analysis. In Cyber Warfare and Terrorism: Concepts, Methodologies, Tools, and Applications (pp. 266-276). IGI Global.
Zhou, G., Zhuge, J., Fan, Y., Du, K. & Lu, S. (2020). A market in dream: the rapid development of anonymous cybercrime. Mobile Networks and Applications, 25(1), 259-270.