Enhancing Cybersecurity Through Multi-Model AI Tracking Systems

Abstract

In the dynamic landscape of cybersecurity, the ability to track and respond to evolving threats in real-time is paramount. This paper explores the utilization of multiple artificial intelligence (AI) models and techniques to bolster tracking capabilities in cybersecurity. By harnessing the power of machine learning, deep learning, and other AI methodologies, we delve into the development of comprehensive tracking systems capable of identifying and mitigating cyber threats across diverse attack vectors. Through case studies and applications, we examine how AI-driven tracking facilitates threat actor attribution, malware propagation tracking, insider threat detection, and network traffic analysis for anomaly detection. Furthermore, we discuss the challenges and future directions in leveraging AI for tracking, including data quality, scalability, adversarial attacks, and privacy considerations. This research sheds light on the potential of multi-model AI tracking systems to strengthen cybersecurity defenses and safeguard digital assets in an increasingly complex threat landscape.