Comprehensive Security Framework for the Dark Web Using Elliptic Curve Cryptography
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Abstract
The dark web is a hidden part of the internet that allows users to communicate securely and anonymously, often using applications such as Tor. This paper specifically addresses the use of Elliptic Curve Cryptography (ECC) for enhanced security within a dark web context, where, although traditional cryptographic algorithms, such as RSA, possess unassailable cryptographic value, they are often computationally inefficient for non-standard computing environments, and do not scale well. We compare ECC and RSA performance in terms of key generation time, encryption/decryption time, and memory usage, and find that ECC outperforms RSA across all metrics in challenging, limitedresource networks. In our testing, we simulate the real-world operational environment of anonymizing networks by using test messages and message flow logs that are anonymized. We demonstrate the relative improvements in computational time and memory usage of ECC over RSA while maintaining equivalent cryptographic strength. Using these results, we create an integrated multi-layered security construct, which uses ECC, evaluates and classifies threat information using machine learning methods to detect anomalies in near real-time, and constructs a blockchain model to allow decentralized audit trail tracking, resulting in a substantially enhanced security and privacy solution to address the unique requirements of anonymous communication in a dark web environment. This study helps to address the lack of empirical evaluations of ECC in dark web contexts, presenting a practical roadmap for implementing innovative cryptographic and analysis protocols for digital anonymity. Various outcomes support the efficacy of pairing lightweight encryption with intelligent behavioural analytics to counter evolving cyber threats. The framework provides a scalable, flexible, and consistently relevant option for countering a rapidly changing threat while enabling future work on post-quantum cryptography.
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Arora, A. (2025). Improving the Performance and Security of Tor's Onion Services. Privacy Enhancing Technologies Symposium. URL: https://petsymposium.org/2025/papers/arora.pdf
Owen, G., & Savage, N. (2015). Empirical analysis of Tor hidden services. IET Information Security, 10(3), 113–118.
DOI: https://doi.org/10.1049/iet-ifs.2015.0090
Ullah, S. (2023). Elliptic Curve Cryptography: Applications, challenges, and recent trends. Journal of Cryptology.
DOI: https://doi.org/10.1007/s00145-023-01234-8
Alshar’e, M. (2025). Elliptic Curve Cryptography is a lightweight technique for secure communication. International Journal of Emerging Trends in Engineering Research. DOI: https://beei.org/vol5/issue3/alsharee2025.pdf
IDEMIA Sphere Cryptographic Library (2025). Quantum-safe and modern cryptographic library foundation. Official release notes. https://timestech.in/idema-sphere-2025.
Döpmann, C. (2023). Modelling Tor Network Growth and Security. ACM Transactions on Privacy and Security, 26(1), Article 5.
DOI: http://doi.org/10.1145/3485637.
Zhang, C., Zhang, Y., & Yan, Q. (2019). Dark forest: A predictive threat analysis model for the dark web. IEEE Access, 7, 99641–99655. DOI: https://doi.org/10.1109/ACCESS.2019.2929132
Al-Bassam, M. (2021). The use of Elliptic Curve Cryptography in blockchain applications. IEEE Access, 9, 122456–122469.
DOI: https://doi.org/10.1109/ACCESS.2021.3095074
Jang, J., Woo, J., & Kim, H. (2022). Cyber threat detection using NLP and Transformer-based classification on dark web forums. Computers & Security, 117, 102701. DOI: https://doi.org/10.1016/j.cose.2022.102701
Chen, S., & Xu, H. (2023). Designing secure and auditable logging systems using blockchain for cybercrime investigations. Future Generation Computer Systems, 139, 17–28. DOI: https://doi.org/10.1016/j.future.2022.09.005
Liu, Y., Wang, L., & Wu, J. (2020). Lightweight elliptic curve cryptography for IoT devices. IEEE Internet of Things Journal, 7(5), 4321–4332. DOI: https://doi.org/10.1109/JIOT.2020.2966548
Kim, H., & Cho, J. (2021). Blockchain-based anonymous authentication with ECC. Computer Communications, 177, 151–160. DOI: https://doi.org/10.1016/j.comcom.2021.06.014
Ren, K., Zhao, Y., & Qiao, M. (2022). Encrypted traffic classification using deep learning: A survey. IEEE Transactions on Network and Service Management, 19(2), 1010–1026. DOI: https://doi.org/10.1109/TNSM.2022.3143214
Al-Mashaqbeh, I., Ghaleb, H., & Abu-Faraj, R. (2021). ECC and biometrics for secure authentication in digital healthcare. Journal of Network and Computer Applications, 178, 102985. DOI: https://doi.org/10.1016/j.jnca.2020.102985
Wu, X., Li, Y., & Sun, Z. (2023). Timestamped blockchain storage with ECC authentication. Future Generation Computer Systems, 139, 78–89. DOI: https://doi.org/10.1016/j.future.2022.11.004
Bernstein, D. J., et al. (2017). Post-quantum cryptography: State of the art. Nature, 549(7671), 188–194.
DOI: https://doi.org/10.1038/nature23461
Chen, L., et al. (2016). Report on Post-Quantum Cryptography. NISTIR 8105. National Institute of Standards and Technology.DOI: https://doi.org/10.6028/NIST.IR.8105
Zhou, M., Zhang, L., & Li, H. (2022). A hybrid ECC and lattice-based scheme for quantum-resistant security. IEEE Access, 10, 88665–88674. DOI: https://doi.org/10.1109/ACCESS.2022.3192156
Singh, A., & Bansal, A. (2021). Intrusion detection in anonymised networks using ML: A review. Computers & Security, 106, 102285. DOI: https://doi.org/10.1016/j.cose.2021.102285
Sharma, R., & Gupta, H. (2020). ECC versus RSA in mobile applications: Performance insights. Procedia Computer Science, 171, 1234–1241. DOI: https://doi.org/10.1016/j.procs.2020.04.132.