Quantum-Resilient Federated Intrusion Detection: A Hybrid Quantum Classical Framework For Safeguarding U.S. Critical Infrastructure In The Post-Quantum Era

Abidul Alam; Md. Salahuddin Gazi; Salman Mohammad  Abdullah; Masrufa  Tasnim; Md  Himeluzzaman; Mahbub Ahmed Nabil; Kabita Ahamia  Akter; Sonia Akter

doi:10.29284/3xx46j76

Authors

Abidul Alam Bachelor of Business Administration (BBA) in Marketing, International University of Business Agriculture and Technology (IUBAT), Dhaka, Bangladesh.
Md. Salahuddin Gazi B.S.S. (Honours) in Sociology, Jagannath University, Dhaka, Bangladesh.
Salman Mohammad Abdullah B.Sc. in Computer Science and Engineering (CSE), Daffodil International University, Bangladesh.
Masrufa Tasnim Master of Business Administration (MBA) in Human Resource Management, University of Dhaka, Dhaka, Bangladesh
Md Himeluzzaman B.Sc. in Computer Science and Engineering, Leading University, Sylhet, Bangladesh
Mahbub Ahmed Nabil Bachelor's in Computer Science and Engineering, North South University, Dhaka, Bangladesh
Kabita Ahamia Akter Bachelor's Degree in Human Resource Management, American International University-Bangladesh (AIUB), Dhaka, Bangladesh
Sonia Akter Bachelor of Business Administration in Management, Narsingdi Government College, Dhaka, Bangladesh

DOI:

https://doi.org/10.29284/3xx46j76

Keywords:

quantum machine learning; federated learning; intrusion detection; variational quantum circuits; post-quantum cryptography; critical infrastructure security; NSL-KDD.

Abstract

Large scale quantum computation poses a threat to the public-key based encryption that protects the privacy of communications traversing U.S. critical infrastructure and defense networks. This property strongly motivates detection systems against “harvest-now, decrypt-later” adversaries and is not compatible with approaches that limit themselves to either centralizing sensitive traffic or searching for classical decision boundaries. We introduce a quantum-resilient federated intrusion-detection solution that combines a hybrid quantum-classical classifier—a classical feature encoder feeding the expectation-value outputs of a variational quantum circuit (VQC) into a classical decision head—with the federated averaging scheme operating across physically distributed infrastructure sites, such that raw traffic never leaves its site of origin. You are the eighth of its kind, specifically designed to receive a post-quantum key-encapsulation mechanism.

The proposed federated hybrid model achieves a 77.65% accuracy and a macro-averaged F1 of 0.6200 on the NSL-KDD benchmark (5-class attack taxonomy), outperforming both centralized and federated classical multilayer-perceptron baselines on macro-F1(0.5975 and 0.5949 respectively) and significantly outperforms a Random-Forest baseline (0.4734), indicative of improved discrimination of rare Remote-to-Local(R2L) and User-to-Root(U2R) attacks The accuracy−cost frontier and the degradation due to non-independent, identically distributed partitions is quantified via ablations over qubit count, circuit depth, client population and data heterogeneity. We honestly report training-cost overheads, and discuss examples where most of the performance improvements come from traversing a barren plateau at greater depth circuits. The results indicate that near-term variational quantum models can be part of an intrusion detection system that is privacy-preserving and sensitive to rare classes while the surrounding protocol is hardened to the post-quantum world.

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