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REVIEW 4 major objections 4 minor 16 references

Proof of AutoML: SDN based Secure Energy Trading with Blockchain in Disaster Case

T0 review · 4 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read The paper claims that AutoML-trained regression models can generate blockchain nonce values from network telemetry, with Random Forest and Extra Trees reaching 100% on its randomness metric.

desk verdict The paper's randomness metric measures output uniqueness, not cryptographic unpredictability, and its nonce generation is deterministic, so the central claim fails—though the idea of using AutoML regressors for nonces is genuinely new. read the letter →

arxiv 2509.10291 v1 pith:CS4SP6AP submitted 2025-09-12 cs.LG cs.NI

Salih Toprak , Muge Erel-Ozcevik This is my paper · ORCID
classification cs.LGcs.NI
keywords BlockchainNoncegenerationAutoMLRegressionmodelsRandomnessrateSDNEnergytradingDisasterresilience
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

After a disaster, households with solar power can trade energy with mobile charging units, but such trades need a blockchain that works without centralized coordination. The authors argue that the nonce values blockchain consensus normally obtains from brute-force proof-of-work can instead be produced by machine-learning regression models running in SDN controllers. They train five AutoML-selected regressors on quality-of-service network features and measure each model's 'randomness rate'—the share of unique predictions in a test set. Random Forest and Extra Trees reach 100% uniqueness and maximum Shannon entropy, and the other three models score above 97%, which the paper takes as evidence that lightweight, context-sensitive nonce generation is feasible in degraded networks.

What carries the argument

The load-bearing device is the 'randomness rate' metric of Eq. (1), the proportion of unique model predictions in the test set, paired with Shannon entropy (Eq. 2) as a uniformity measure. These metrics recast a regressor's output diversity as a cryptographic qualification. The AutoML regressors themselves—five models selected via the PyCaret pipeline—are the generators: throughput is the target proxy, and the four QoS features (delay, jitter, packet loss, throughput) provide the context-dependent variation that yields nonce candidates within the SDN controller.

What would settle it

Run the trained Random Forest or Extra Trees regressor twice on the same network-state vector and observe that it yields the identical nonce; or feed a long sequence of model-generated nonces to a standard statistical randomness test suite (e.g., NIST SP 800-22) and find that it fails.

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Extended reading notes

Core claim

The central claim is that predictive variability in regression models is a usable source of blockchain nonce values. On a 9000-sample dataset of one-way delay, jitter, packet loss, and throughput, five AutoML-selected regressors were trained to predict throughput. The authors intentionally disregard prediction accuracy and instead evaluate the diversity of outputs, defining randomness rate as the percentage of unique predictions. Random Forest and Extra Trees produced 100% unique outputs with Shannon entropy of 1.0, Gradient Boosting 97.6%, KNN 98.8%, LightGBM 99.9%. The paper interprets these scores as showing that tree-based ensembles can serve as lightweight nonce generators in an SDN-ass

Load-bearing premise

The central claim collapses if output uniqueness on a fixed test set is taken as cryptographic unpredictability, since a deterministic regressor reproduces the same output when the same feature vector appears again.

Editorial extensions

If this is right

  • In a disaster, SDN controllers can generate nonce values from network telemetry, avoiding the computational cost of proof-of-work brute-force search.
  • Random Forest and Extra Trees regressors produced 100% unique outputs and maximum Shannon entropy on the test set, making them the paper's recommended nonce generators.
  • The other three models—Gradient Boosting, K-Nearest Neighbors, LightGBM—also exceeded 97% randomness and high entropy, suggesting a spectrum of workable candidates.
  • The paper's trade-off analysis implies that models optimized for predictive accuracy (e.g., Gradient Boosting) are not necessarily the best for nonce generation, and vice versa.
  • All five models are claimed to pass the randomness and unpredictability thresholds the authors consider necessary for secure blockchain integration.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extension: Uniqueness on a fixed test set is not the same as unpredictability to an adversary; a deterministic regressor produces identical output when the same feature vector reoccurs, so the generated nonce is reproducible by anyone with the model and the network state.
  • Extension: A more direct test of the paper's claim would be to feed identical inputs repeatedly and check for identical outputs, or to run a standard cryptographic randomness battery (e.g., NIST SP 800-22) on a long sequence of generated nonces.
  • Extension: The observed 'randomness' may largely reflect the diversity of feature vectors in the test set rather than an intrinsic property of the model; reshuffling or resampling the same data would likely change the metric.
  • Extension: If deployed, the security of this scheme would rest on the secrecy of the model or its input features—an unusual trust assumption for a public blockchain ledger.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes an SDN-assisted blockchain architecture for secure energy trading in disaster scenarios, in which AutoML-selected regression models (Gradient Boosting, LightGBM, Random Forest, Extra Trees, K-Nearest Neighbors) are used to generate nonce candidates. Using a 9000-sample QoS dataset, the authors evaluate the models on standard regression metrics plus a proposed 'randomness rate' defined as the proportion of unique predictions, and Shannon entropy. They report that Random Forest and Extra Trees achieve 100% randomness and maximum normalized entropy, concluding that these deterministic regression models can serve as lightweight, unpredictable nonce generators for blockchain consensus.

Significance. If the proposed mechanism were sound, replacing proof-of-work with ML-generated nonces on SDN controllers could reduce computational overhead in energy-constrained disaster environments. The paper provides a clearly described architecture and benchmarks five models, including training and inference times. It does not, however, provide any security analysis, standard randomness testing, or reproducible experimental details. The central claim rests on a metric that conflates output uniqueness with cryptographic unpredictability, so the contribution, as presented, does not establish a valid nonce-generation method.

major comments (4)
  1. [IV-C, Eq. (1)] The randomness rate R = U / (Σ P_i) × 100, defined as the fraction of unique predictions in the test set, is not a measure of unpredictability. For continuous regression outputs, floating-point predictions are almost surely unique whenever input feature rows are distinct, so the 100% values in Table II likely reflect the absence of duplicate feature vectors rather than any stochastic property. Moreover, Algorithm 1 uses nonce = AutoML.predict(NetworkFeatures), a deterministic function; anyone with the model and network telemetry can reproduce the nonce. The paper offers no adversary model, no secret entropy input, and no standard tests such as NIST SP 800-22 or TestU01. The conclusion that these models are suitable nonce generators is therefore unsupported. The equation itself is also not well-formed: the text defines P as 'the number of unique predictions' while using ΣP_i as the total
  2. [IV-C, Eq. (2)] The Shannon entropy calculation is applied to raw floating-point predictions without specifying any binning or discretization. With continuous outputs, the empirical distribution assigns probability near 1/N to each distinct value, so H(X) ≈ log2 N; after normalization to [0,1] this is near 1.0000 by construction. Thus the entropy values in Table II provide no independent evidence of uniformity or unpredictability. The authors must specify a quantization scheme and compare the entropy against the theoretical maximum for the chosen bins. As written, the entropy result is an artifact of the evaluation procedure.
  3. [IV-D, Algorithm 1] The mining process lacks any security analysis. The block hash includes a 'predicted nonce' from a deterministic AutoML model; if network features are observable or estimable, an adversary can precompute candidate nonces and craft valid-looking blocks. The paper does not analyze collision resistance, nonce reuse, resistance to feature manipulation, or double-spending. Claiming that the method 'eliminates the need for PoW or deterministic nonce generation' is unjustified without a threat model and a formal or simulation-based security argument.
  4. [V, Table II] The experimental basis is thin. The dataset is described only as '9000 samples' with no source, generation process, feature distributions, train/test split, or number of repeated runs. The randomness rates are point estimates from a single test set, with no confidence intervals or sensitivity analyses (e.g., to floating-point precision, input perturbations, or test-set size). Since these numerical results are the sole support for the central claim, the manuscript needs at least the data-generation procedure and code to be reproducible.
minor comments (4)
  1. [IV-C, Eq. (1)] Typographical and notation issues: the definition of P and U should be clarified; as printed, R = U / (Σ P_i) × 100 is ambiguous and not dimensionally consistent with the prose.
  2. [Abstract and IV-D] The terms 'non-deterministic' and 'randomness' are used to describe outputs of deterministic regression models. Recommend replacing these with 'diverse' or 'pseudo-random' to avoid an internal contradiction.
  3. [References] Several references have inconsistent formatting (e.g., [8] is listed with a Sensors DOI, while the in-text citation and surrounding references use IEEE style). Please unify the reference list.
  4. [V] The paper does not state whether the reported randomness rates are based on the same train/test split used for the accuracy metrics, nor how shuffling was applied. This should be specified for reproducibility.

Circularity Check

2 steps flagged · score 7.0 of 10

Randomness rate and entropy are defined from output uniqueness, so the headline 'complete dependency on randomness' is entailed by definition; no independent randomness test supports nonce suitability.

  1. self definitional [Section IV-C, Eq. (1) and Table II]
    "we introduce a metric called randomness rate, calculated as: R= U/PNi=1 Pi ×100 ... A randomness rate closer to 100% indicates that the model rarely generates duplicate values, which is a desirable trait in nonce generation."

    The paper defines 'randomness rate' as the fraction of unique predictions in the test set, then interprets high R as evidence that the model output is random and suitable as a nonce. This reduces randomness to uniqueness by construction: the metric contains no temporal, adversarial, or statistical unpredictability component. On continuous regression outputs, 100% uniqueness is essentially a property of floating-point distinctness or of test feature rows being distinct, not of stochasticity. The conclusions that Random Forest and Extra Trees exhibit 'complete dependency on randomness' therefore restate the definition of the metric rather than providing independent evidence.

  2. self definitional [Section IV-C, Eq. (2) and Table II]
    "In addition to randomness rate, we employ Shannon entropy to quantify the unpredictability of the model outputs [16]. Shannon entropy is defined as: H(X) =− nX i=1 p(xi) log2 p(xi)(2) where p(xi) is the probability of observing the prediction value xi. Higher entropy values, normalized to the range [0,1], indicate more uniformly distributed and less predictable outputs."

    With no binning specified, p(x_i) is the empirical frequency of exact floating-point predictions. If all N predictions are unique, p(x_i)=1/N, so H=log2 N, which normalizes to 1.0000. The paper's Table II then reports 'maximum Shannon entropy of 1.0000, indicating ... highly unpredictable predictions'. The maximum is forced by the uniqueness already counted in Eq. (1); it does not measure order, compressibility, or adversarial predictability of the sequence. Thus the entropy evidence is the same circular uniqueness input in another form.

full rationale

The central claim that AutoML regression models are suitable blockchain nonce generators is supported by two metrics, both of which define the conclusion into the measurement. Eq. (1) calls the proportion of unique predictions the 'randomness rate'; Eq. (2) applies Shannon entropy to the same unique floating-point outputs, which yields the maximum normalized value whenever Eq. (1) is 100%. The paper even states that 'a randomness rate closer to 100% indicates that the model rarely generates duplicate values, which is a desirable trait in nonce generation' -- uniqueness is the criterion, and uniqueness is what is measured. No independent randomness benchmark (e.g., NIST SP 800-22, TestU01) is applied, and Algorithm 1 generates the nonce by deterministic AutoML.predict(NetworkFeatures), so the 'non-deterministic outputs' characterization does not rescue the inference. The architecture, training, and latency measurements are independent contributions, but the load-bearing suitability result reduces by construction to uniqueness. Hence a score of 7: substantial definitional circularity in the central randomness analysis, while some non-circular engineering content remains. Self-citations are not load-bearing here.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on three unstated domain assumptions: dataset representativeness, the equivalence of output uniqueness with cryptographic randomness, and the validity of entropy computed on raw continuous predictions. There are no fitted free parameters in the explicit sense, but the metric itself embeds the desired conclusion. No new physical entities or objects are introduced.

assumptions (3)
  • domain assumption The 9000-sample QoS dataset is representative of post-disaster network states.
    No description of how the data were generated, synthetic or real, or its realism. The paper says the parameters are 'readily available' and 'strongly influenced by real-world conditions' (Section IV-A), but provides no evidence of disaster-case representativeness.
  • domain assumption Unique model outputs are sufficient for a blockchain nonce.
    The paper equates a high uniqueness rate with nonce suitability without analyzing predictability or adversarial knowledge of the deterministic model. This is the central modeling assumption, invoked in Section IV-C and used to interpret Table II.
  • domain assumption Shannon entropy computed on prediction values is a measure of unpredictability.
    Entropy requires a well-defined probability distribution; the paper does not specify binning or quantization of the continuous outputs. Near-unique floating-point values trivially yield high entropy, so this assumption is not justified.

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Cite this review

Pith. "Pith review of Proof of AutoML: SDN based Secure Energy Trading with Blockchain in Disaster Case." pith.science (2026). https://pith.science/paper/CS4SP6AP

@misc{pith2026250910291,
  author       = {Pith},
  title        = {Pith review of: Proof of AutoML: SDN based Secure Energy Trading with Blockchain in Disaster Case},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CS4SP6AP}},
  note         = {Machine review of arXiv:2509.10291}
}
read the original abstract

In disaster scenarios where conventional energy infrastructure is compromised, secure and traceable energy trading between solar-powered households and mobile charging units becomes a necessity. To ensure the integrity of such transactions over a blockchain network, robust and unpredictable nonce generation is vital. This study proposes an SDN-enabled architecture where machine learning regressors are leveraged not for their accuracy, but for their potential to generate randomized values suitable as nonce candidates. Therefore, it is newly called Proof of AutoML. Here, SDN allows flexible control over data flows and energy routing policies even in fragmented or degraded networks, ensuring adaptive response during emergencies. Using a 9000-sample dataset, we evaluate five AutoML-selected regression models - Gradient Boosting, LightGBM, Random Forest, Extra Trees, and K-Nearest Neighbors - not by their prediction accuracy, but by their ability to produce diverse and non-deterministic outputs across shuffled data inputs. Randomness analysis reveals that Random Forest and Extra Trees regressors exhibit complete dependency on randomness, whereas Gradient Boosting, K-Nearest Neighbors and LightGBM show strong but slightly lower randomness scores (97.6%, 98.8% and 99.9%, respectively). These findings highlight that certain machine learning models, particularly tree-based ensembles, may serve as effective and lightweight nonce generators within blockchain-secured, SDN-based energy trading infrastructures resilient to disaster conditions.

Figures

Figures reproduced from arXiv: 2509.10291 by the authors.

Figure 1
Figure 1. Proposed Proof of AutoML architecture for secure energy trading in disaster scenarios. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Proposed SDN-assisted blockchain architecture. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

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