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

Mohammad Sayduzzaman, Anichur Rahman, Jarin Tasnim Tamanna, Dipanjali Kundu, Tawhidur Rahman

TL;DR

The paper tackles zero-day attack detection in smart-city contexts by proposing a three-layer interoperability and explainable AI (XAI) based IDPS architecture that integrates 6G, IoE, and WiFi-8. The intermediate layer uses SHAP-based explainable insights to reduce a large feature set to 15 key attributes and to identify novel attack patterns, which are then passed to the final IDPS layer for residual anomaly detection. Results indicate high attack-pattern detection accuracy (around 94.89%) and improved anomaly-detection efficiency when applying XAI, outperforming several baselines. This approach enables rapid threat sharing and automated response across heterogeneous networks, with potential for deployment in large-scale smart communities and future enhancement via larger datasets.

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.

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

TL;DR

The paper tackles zero-day attack detection in smart-city contexts by proposing a three-layer interoperability and explainable AI (XAI) based IDPS architecture that integrates 6G, IoE, and WiFi-8. The intermediate layer uses SHAP-based explainable insights to reduce a large feature set to 15 key attributes and to identify novel attack patterns, which are then passed to the final IDPS layer for residual anomaly detection. Results indicate high attack-pattern detection accuracy (around 94.89%) and improved anomaly-detection efficiency when applying XAI, outperforming several baselines. This approach enables rapid threat sharing and automated response across heterogeneous networks, with potential for deployment in large-scale smart communities and future enhancement via larger datasets.

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.
Paper Structure (11 sections, 1 equation, 8 figures, 1 table)

This paper contains 11 sections, 1 equation, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Proposed Methodology for Interoperability and Explainable Artificial Intelligence (XAI)-based IDPS
  4. Generic Layer Discussion with Interoperability and Zero-Day Attacks
  5. Intermediate Layer Detection through ML and XAI Training Process
  6. Final Layer Anomaly Detection with IDPS Technique
  7. Datasets Description
  8. Result Analysis and Performance Measurement
  9. ML Model and Accuracy of Attack Patterns Analysis
  10. Accuracy and Time of Anomaly Analysis
  11. Conclusion

Figures (8)

  • Figure 1: Concept of Zero-day attack detection through interoperability, and AI
  • Figure 2: Life Cycle of a Zero Day Attack
  • Figure 3: Proposed Architecture for Interoperability and XAI-based IDPS
  • Figure 4: SHAP values for Attack Pattern Analysis
  • Figure 5: SHAP values for Anomaly Analysis
  • ...and 3 more figures