Securing the Financial Realm: Unveiling Cybersecurity Threats, Vulnerabilities, and Advanced Security Solutions in Banking

Abstract

With the increasing reliance on digital platforms for financial transactions, the security of banking systems has become a critical concern. Intrusion detection plays a pivotal role in safeguarding these systems against unauthorized access and malicious activities. This research paper explores the application of Support Vector Machines (SVMs) as a robust and efficient tool for detecting intrusions in banking systems. SVMs, known for their ability to handle high-dimensional data and nonlinear patterns, are employed to enhance the accuracy and reliability of intrusion detection in the complex and dynamic banking environment. Existing intrusion detection methods struggle to cope with the diverse and evolving nature of cyber threats. This research is motivated by the potential of SVMs to provide a solution, given their capacity to classify intricate patterns and adapt to changing environments.