in cybersecurity, limited attention is given to secure knowledge-sharing among
employed defensive ML models, and requires immediate attention.

##  6\. Conclusion

As our world becomes more interconnected and reliant on digital systems, the
imperative to safeguard our information and digital infrastructure has never
been greater. The relentless evolution of cyber threats has compelled us to
explore innovative solutions, and among these, GNN has emerged as a beacon of
promise. This paper has delved into the applications of GNNs in disrupting
each stage of the Cyber Kill Chain, a well-established attack life-cycle
framework for understanding cyberattacks. By addressing each phase and
elucidating how GNNs bolster defensive cyber operations, we have underscored
the potential of this technology. However, it is crucial to acknowledge that
the cyber landscape is dynamic and ever-changing, and GNNs also need to adapt
to the change. Undoubtedly there are open research areas and avenues for
further improvements. This ongoing exploration is vital to stay ahead of
increasingly sophisticated adversaries. With this essence, this paper
underscores the contributions and further research directions for GNN to
defend against cyber threats effectively.

###### Acknowledgements.

This work was supported by PATENT Lab (Predictive Analytics and TEchnology
iNTegration Laboratory) at the Department of Computer Science and Engineering,
Mississippi State University. We also acknowledge Kyanie Waters and La’Andrea
Gates for their contributions.

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