Advanced Machine Learning Models for Cybersecurity Threat Mitigation in the US Digital Landscape

Аннотация

This paper explores the consolidation of advanced machine learning (ML) algorithms in combating cybersecurity threats within the US digital landscape, underscoring their transformative capability in elevating threat detection and response. It covers traditional cybersecurity measures that can no longer address these issues and a proactive approach that must be data-driven. The paper examines Machine Learning techniques, unsupervised, reinforcement learning, and deep learning, and their application in threat detection, user behavior analytics, malware classification, and automated response. Benefits derived from ML include an increase in the accuracy of detection, rapid response towards emerging threats, scalability, and better decision-making. Precariously discussed are some challenges on data quality, algorithmic bias, complexities, and integration with existing infrastructure. The paper concludes by highlighting priorities for further research in blockchain integration, quantum Machine Learning, collaboration, and the need to continuously update ML technologies within a constantly changing threat landscape. Key Words: Cybersecurity; Threat Detection; Malware Classification; Machine Learning; Incident Response; Data Privacy