Enhancing E-Commerce Transaction Security with Big Data Analytics in Cloud Computing

Cloud Computing

With the rapid increase in the number of e-commerce transactions, security measures must be considerably stronger to protect sensitive user and financial information. This research develops a new framework to secure e-commerce transactions using a combination of cloud computing and big data analytics. The framework which integrates blockchain-based ledger verifications, ML-based pattern recognition for fraud detection and real-time detection of anomalies related to customer data, addresses the current demands of cyber threats. This type of distributed cloud infrastructure and predictive analytics brings low latency and high assurance for secure transactions. The findings illustrate significant advancements in both threat detection rates and computational efficiency over conventional security models. Thus, they have made an essential contribution to safeguarding the integrity of digital transactions which proves to be fundamental to countering pressing vulnerabilities in today's e-commerce environments. The framework employs access control methodologies and advanced encryption schemes enhancing the level of privacy assurance for individual data. Experimental validation demonstrates that our framework can identify fraudulent transactions with high confidence and at scale while efficiently executing system requirements.

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