AUTONOMOUS THREAT INTELLIGENCE AND MACHINE LEARNING-BASED CYBER DEFENSE MECHANISMS FOR SECURING IOT AND EDGE COMPUTING ECOSYSTEMS

Abstract

The proliferation of the Internet of Things (IoT) and Edge Computing has significantly expanded the attack surface for cyber threats. Traditional security mechanisms struggle to keep pace with the evolving sophistication of cyberattacks. Autonomous threat intelligence, coupled with machine learning (ML)-based cyber defense mechanisms, provides a proactive approach to securing these ecosystems. This paper explores state-of-the-art techniques in automated cyber defense, including anomaly detection, threat prediction, and response automation using AI-driven models. A literature review highlights recent advancements in ML-based cybersecurity frameworks, while a tree diagram illustrates the layered approach to security in IoT and Edge environments. This research presents empirical data supporting the efficiency of AI-based threat mitigation techniques. The study concludes that integrating machine learning with autonomous threat intelligence significantly enhances security resilience, offering a scalable and adaptive defense strategy for modern IoT ecosystems.