• Srushti Kantesh Kharat
• Sufiyan Patel

The widespread adoption of blockchain-based payment gateways has significantly transformed digital financial ecosystems by enabling decentralized, transparent, and tamper-resistant transactions. Despite these advantages, blockchain payment gateways remain vulnerable to adversarial strategies that exploit probabilistic transaction finality, network latency, and consensus assumptions. Attacks such as double spending, transaction malleability, chain reorganization, selfish mining, and timing-based exploits pose serious risks to merchants and users, particularly in real-time payment scenarios. Existing gateway architectures largely assume honest network participation and static confirmation thresholds, which are insufficient under dynamic adversarial conditions. This research proposes BLACKCHAIN, a formal and adversary-aware framework designed to enhance resilience in blockchain-based payment gateways. The framework introduces structured adversarial threat modeling, multi-layer transaction validation, adaptive confirmation mechanisms, and continuous anomaly monitoring at the gateway layer. By integrating blockchain security principles with resilience engineering and adversarial analysis, BLACKCHAIN provides a robust, scalable, and secure payment gateway architecture. The framework aims to strengthen transactional integrity, reduce fraud probability, and improve trust in decentralized payment infrastructures.

"BLACKCHAIN: A FORMAL FRAMEWORK FOR ADVERSARIAL RESILIENCE IN PAYMENT GATEWAYS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.c598-c602, February-2026, Available :http://www.jetir.org/papers/JETIR2602289.pdf

"BLACKCHAIN: A FORMAL FRAMEWORK FOR ADVERSARIAL RESILIENCE IN PAYMENT GATEWAYS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. ppc598-c602, February-2026, Available at : http://www.jetir.org/papers/JETIR2602289.pdf