Quantum Entropy and Reinforcement Learning for Distributed Denial of Service Attack Detection in Smart Grid
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) threats in Smart Grid are very challenging and considered one of the most destructive cyber-attacks. They are harmfully affecting the power sector and causing substantial financial loss. Classical Reinforcement Learning (RL) models are proposed as a promising solution to the DDoS problem as they learn optimal mitigation policies under different attack scenarios. However, as the RL environment is generally dynamic, which leads to more computation capabilities, classical RL efficiency can be limited as a result of serial processing using classical computing. In this paper, a Quantum Entropy Q-Learning (QEQ) is proposed to fight DDoS in Smart Grid. The proposed framework is compared to a classical Q-Learning (QL) model with and without the Entropy method. The convergence speed and the total rewards performed with the same conditions for QL and are Entropy Q-Learning (EQ) are analyzed to show the QEQ performance. Moreover, the accuracy, precision, recall, and F1 score are evaluated to prove the effectiveness of our QEQ in fighting DDoS attacks. Using the Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS 2019) dataset, a thorough systematic simulation using MATLAB, Python, the open-source Qiskit software, and the Harrow-Hassidim-Lloyd (HHL) quantum algorithm is carried out. This proposed QEQ is better performing the DDoS detection as it is faster enough and more adaptable to dynamic environment changing and able to improve the agent’s decision-making in a changing state action space. Finally, conclusions with some open issues of the Quantum Q-Learning are presented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".