Comprehensive Security Framework for the Dark Web Using Elliptic Curve Cryptography
Sree Vidya Venigalla1, K.V.D Kiran2
1Sree Vidya Venigalla, Student, Department of Computer Science Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada (Andhra Pradesh), India.
2Dr. K.V.D Kiran, Professor, Department of Computer Science Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada, (Andhra Pradesh), India.
Manuscript received on 01 November 2025 | Revised Manuscript received on 06 November 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 8-12 | Volume-14 Issue-12, November 2025 | Retrieval Number: 100.1/ijitee.A119515011225 | DOI: 10.35940/ijitee.A1195.14121125
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Anonymity, Blockchain, Cyber Threats, Dark Web, Encryption, Onion Routing, Tor, Elliptic Curve Cryptography (ECC), Machine Learning, Cybersecurity.
Scope of the Article: Information and Data Security