A Privacy-Preserving Framework for Efficient Network Intrusion Detection in Consumer Network Using Quantum Federated Learning
Bibliographic record
Abstract
The proliferation of consumer networks has increased vulnerabilities to network intrusions, emphasizing the critical need for robust intrusion detection systems (IDS). The data-driven Artificial Intelligence (AI) approach has gained attention for enhancing IDS capabilities to deal with emerging security threats. However, these AI-based IDS face challenges in scalability and privacy preservation. More importantly, they are time-consuming and may perform poorly on high-dimensional and complex data due to the lack of computational resources. To address these shortcomings, in this paper, we introduce a novel framework, called Quantum Federated Learning IDS (QFL-IDS), that merges Quantum Computing (QC) with Federated Learning (FL) to allow for an efficient, robust, and privacy-preserving approach for detecting network intrusions in consumer networks. Leveraging the decentralized nature of FL, QFL-IDS enables multiple consumer devices to collaboratively train a global intrusion detection model while preserving the privacy of individual user data. Furthermore, we leverage the computational power of quantum computing to improve the efficiency of model training and inference processes. We demonstrate the efficacy of our framework through extensive experiments. The obtained results show significant improvements in detection accuracy and computational efficiency compared to the current traditional centralized and federated learning approaches. This makes QFL-IDS a promising framework to cope with the new emerging security threats in a timely and effective manner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".