Federated Artificial Intelligence for Scalable and Privacy-Aware Cyber  Threat Intelligence

Sunil Chandolu, Dr.Pankaj Khairnar

Federated Artificial Intelligence for Scalable and Privacy-Aware Cyber Threat Intelligence

Authors

Sunil Chandolu, Dr.Pankaj Khairnar

Keywords:

Federated Learning, Cyber Threat Detection, Privacy-Preserving Systems, Intrusion Detection Systems, Distributed Networks, Deep Learning, Cybersecurity, IoT Security, Anomaly Detection, Distributed Machine Learning

Abstract

The rapid expansion of distributed computing infrastructures, cloud platforms, Internet ofThings (IoT) ecosystems, and intelligent communication networks has significantly increased the complexity and frequency of cyber threats

References

McMahan, B., et al., “Communication-Efficient Learning of Deep Networks from Decentralized Data,” Proceedings of AISTATS, 2017.

Li, T., et al., “Federated Learning: Challenges, Methods, and Future Directions,” IEEE Signal Processing Magazine, 2020.

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Published

2024-10-15

How to Cite

Sunil Chandolu, Dr.Pankaj Khairnar. (2024). Federated Artificial Intelligence for Scalable and Privacy-Aware Cyber Threat Intelligence . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8716–8723. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5434