Heterogeneous Zero Day Attack

Heterogeneous zero-day attacks, exploiting unknown vulnerabilities in diverse systems, pose a significant challenge to cybersecurity. Current research focuses on developing robust defense mechanisms, often employing multi-agent models, reinforcement learning (RL), and game-theoretic approaches like Stackelberg Security Games, to optimize resource allocation and dynamically adapt to evolving threats. These models are being enhanced with techniques like federated learning to address data privacy concerns and improve scalability for large-scale deployments. The ultimate goal is to create efficient and adaptable security systems capable of mitigating the impact of these unpredictable attacks across various platforms and applications.