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

• URL: http://arxiv.org/abs/2408.02921v1
• Date: Tue, 6 Aug 2024 03:11:36 GMT
• Title: Interoperability and Explicable AI-based Zero-Day Attacks Detection Process in Smart Community
• Authors: Mohammad Sayduzzaman, Jarin Tasnim Tamanna, Dipanjali Kundu, Tawhidur Rahman,
• Abstract summary: This paper aims to explain how future technologies such as 6G mobile communication, Internet of Everything (IoE), Artificial Intelligence (AI), and Smart Contract embedded WPA3 protocol-based WiFi-8 can work together to prevent known attack vectors and provide protection against zero-day attacks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Systems, technologies, protocols, and infrastructures all face interoperability challenges. It is among the most crucial parameters to give real-world effectiveness. Organizations that achieve interoperability will be able to identify, prevent, and provide appropriate protection on an international scale, which can be relied upon. This paper aims to explain how future technologies such as 6G mobile communication, Internet of Everything (IoE), Artificial Intelligence (AI), and Smart Contract embedded WPA3 protocol-based WiFi-8 can work together to prevent known attack vectors and provide protection against zero-day attacks, thus offering intelligent solutions for smart cities. The phrase zero-day refers to an attack that occurs on the day zero of the vulnerability's disclosure to the public or vendor. Existing systems require an extra layer of security. In the security world, interoperability enables disparate security solutions and systems to collaborate seamlessly. AI improves cybersecurity by enabling improved capabilities for detecting, responding, and preventing zero-day attacks. When interoperability and Explainable Artificial Intelligence (XAI) are integrated into cybersecurity, they form a strong protection against zero-day assaults. Additionally, we evaluate a couple of parameters based on the accuracy and time required for efficiently analyzing attack patterns and anomalies.

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