Privacy-Preserving Intrusion Detection in Pharmaceutical Information   Systems Using Federated Learning

Prasanth Alluri

Privacy-Preserving Intrusion Detection in Pharmaceutical Information Systems Using Federated Learning

Authors

Prasanth Alluri

Keywords:

Federated learning; intrusion detection; pharmaceutical information systems; privacy-preserving ML; secure aggregation; differential privacy; adversarial ML

Abstract

Pharmaceutical information systems manage highly sensitive clinical, research,manufacturing, and supply chain data, making them attractive targets for cyber attackswhile operating under strict privacy and regulatory constraints. Traditional centralized intrusion detection systems require aggregating

References

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., C Zhang, L. (2016, October). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 308- 318).

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Published

2023-04-18

How to Cite

Prasanth Alluri. (2023). Privacy-Preserving Intrusion Detection in Pharmaceutical Information Systems Using Federated Learning. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2559–2593. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4954