Zero Day

Zero-day attacks exploit previously unknown software vulnerabilities, posing a significant threat to various systems, from vehicles and IoT devices to online services. Current research focuses on developing robust detection methods, employing diverse machine learning approaches such as autoencoders, deep belief networks, and federated learning, often combined with techniques like behavioral fingerprinting and graph-based analysis of network traffic. These advancements aim to improve the accuracy and speed of zero-day detection, mitigating the risks associated with these unpredictable threats and enhancing overall cybersecurity.