Research on Security Situation Awareness Model Based on Financial Industry Payment Scenario

Abstract

With the rapid development of payment scenarios in the financial industry, cybersecurity threats are escalating, making payment systems a primary target for attackers. These systems face multiple challenges, including data breaches, fraudulent transactions, and malicious attacks. To address these issues, this paper proposes a cyber situational awareness model tailored to payment scenarios in the financial industry, aimed at enhancing the security capabilities of these systems. The model is designed with four layers: data acquisition, data processing, situational awareness, and response decision-making. It integrates multi-source data and applies machine learning algorithms for real-time analysis, achieving precise threat detection and effective response. Experimental results show that the model outperforms existing methods in detection accuracy, response speed, and applicability. It effectively identifies security vulnerabilities in payment systems and enables timely countermeasures. This study provides theoretical support and technical reference for security management in payment scenarios within the financial industry.