BankShield Secure Banking Application | IJCT Volume 12 – Issue 5 | IJCT-V12I5P81

Enhancing Banking Transaction Security Through Remote and Foreign IP Address Analysis With the increasing digitization of financial services, online banking has become a primary channel for fund transfers and payments. However, this convenience has also made banking systems attractive targets for cybercriminals. One of the growing concerns in modern financial cybersecurity is unauthorized or suspicious transactions initiated through remote access and foreign IP addresses. These threats often stem from phishing attacks, credential theft, VPN misuse, or compromised user devices, enabling attackers to conduct fraudulent transactions that bypass traditional security layers. To address these risks, this project proposes a detection and blocking framework based on real-time analysis of IP addresses involved in banking transactions. By correlating IP geolocation, reputation scores, behavioral patterns, and known threat intelligence, the system identifies anomalies—such as a user initiating a transaction from an unusual country, an IP flagged as a proxy or VPN, or a login from multiple countries within a short timeframe. Such behavioral deviation and high-risk indicators are used to flag and block suspicious transactions before execution. The framework integrates IP intelligence APIs, geolocation databases, and user behavior profiling to assess the risk of each transaction. Machine learning models and rule-based engines are employed to detect anomalies based on historic user behavior and real-time session data. Additionally, a centralized dashboard allows administrators to monitor transaction flow, analyze alert logs, and continuously refine the detection rules. The prototype implementation involves simulating transaction data with varied IP contexts, evaluating the model’s ability to correctly flag or allow transactions. Performance metrics such as detection accuracy, false positive rate, and processing latency are measured to validate the system’s effectiveness. The expected outcome of this project is a robust and scalable fraud detection module that improves the security posture of banking systems by proactively identifying and blocking transactions from high-risk remote or foreign sources. This framework is intended to complement existing authentication mechanisms and provide an additional layer of protection against financial cyber threats.

The Bank Shield IP framework addresses one of the most critical challenges in modern digital banking— preventing fraudulent transactions initiated from suspicious or foreign IP addresses. By combining IP intelligence, geolocation analysis, and behavioral profiling, the system provides a proactive defense layer capable of detecting and blocking high-risk activities before they compromise user accounts or institutional integrity. Through the integration of real-time IP monitoring, rule-based decision engines, and machine learning models, the framework demonstrates the potential to significantly enhance the cybersecurity posture of financial institutions. Its modular design ensures flexibility, scalability, and compatibility with existing banking infrastructures, making it a viable addition to current fraud detection systems. The evaluation of the prototype highlights the importance of continuous learning and dynamic threat adaptation in reducing false positives and improving detection accuracy. Overall, Bank Shield IP represents a forward-looking step toward intelligent, automated, and adaptive transaction security—strengthening trust in digital banking and paving the way for safer, more resilient financial ecosystems.

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