Enhance Telecommunication Security Through the Integration of Support Vector Machines

Abstract

This research investigates the escalating issue of telephone-based fraud in Indonesia, a consequence of enhanced connectivity and technological advancements. As the telecommunications sector expands, it faces increased threats from sophisticated criminal activities, notably voice call fraud, which leads to significant financial losses and diminishes trust in digital systems. This study presents a novel security system that leverages the capabilities of Support Vector Machines (SVM) for the advanced classification of complex patterns inherent in fraudulent activities. By integrating SVM algorithms, this system aims to effectively process and analyze large volumes of data to identify and prevent fraudulent acts. The utilization of SVM in our proposed framework represents a significant strategy to combat the adaptive and evolving tactics of cybercriminals, thereby bolstering the resilience of telecommunications infrastructure. Upon further refinement, the system exhibited a substantial improvement in identifying fraudulent activities, with accuracy rates increasing from 81% to 86%. This enhancement underscores the system's efficacy in real-world scenarios. Our research underscores the critical need to marry technological innovations with ethical and privacy considerations, highlighting the role of public awareness and education in augmenting security measures. The development of this SVM-based security system constitutes a pivotal step towards reinforcing Indonesia's telecommunications infrastructure, contributing to the national objective of securing the digital economy and fostering a robust digital ecosystem. By addressing current and future cyber threats, this approach exemplifies Indonesia's commitment to leveraging technology for societal welfare, ensuring a secure and prosperous digital future for its citizens.