Enhancing Multi-Factor Authentication Security Through Speech Recognition and Pattern Detection for Malicious Activity in Computer Systems

Abstract

In order to strengthen security against malicious activity and malware attacks in computer systems, this research study explores the integration of speech recognition and pattern detection algorithms into multi-factor authentication (MFA) systems. Acknowledging the growing risks in the digital domain, we tackle the drawbacks of conventional MFA and provide a new strategy that combines sophisticated pattern detection algorithms with the distinctive qualities of human speech. Our technology seeks to offer a robust and intuitive authentication solution by means of speech feature analysis and anomaly pattern detection. The experimental results reveal that the integrated strategy works well, exhibiting increased accuracy and having the ability to improve computer systems' overall security posture. This work provides new opportunities for investigating the security of digital settings while also advancing MFA technology.