Interoperability and Explicable AI-based Zero-Day Attacks Detection Process in Smart Community

Interoperability between security systems is essential for effective protection: The paper emphasizes that organizations achieving interoperability across different security solutions and technologies can "identify, prevent, and provide appropriate protection on an international scale," suggesting that siloed security systems are insufficient for modern threats.

Explainable AI (XAI) is critical for trustworthy cybersecurity: The authors argue that integrating Explainable Artificial Intelligence into cybersecurity—rather than using "black box" AI systems—creates "a strong protection against zero-day assaults," implying that security professionals and stakeholders need to understand why an AI system flagged a threat, not just that it did.

AI can detect previously unknown attacks: The paper highlights that AI improves cybersecurity by enabling "improved capabilities for detecting, responding, and preventing zero-day attacks"—attacks that exploit vulnerabilities unknown to vendors or the public—suggesting AI systems can identify suspicious patterns even when traditional signature-based detection fails.

Future smart city infrastructure requires integrated security across multiple technologies: The authors propose that emerging technologies like 6G, Internet of Everything (IoE), WiFi-8, and smart contracts must work together seamlessly to protect smart communities, indicating that responsible AI deployment in smart cities cannot be addressed in isolation.

Performance metrics matter for practical deployment: The paper evaluates "accuracy and time required for efficiently analyzing attack patterns," suggesting that responsible AI systems must balance detection effectiveness with speed to be viable in real-world security operations.