Initial upload: 68,885 configs + paper v2 with FP-Stalker block

.gitattributes

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+cano_evaluations.csv filter=lfs diff=lfs merge=lfs -text
+cano_paper_v2.pdf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-sa-4.0
+language:
+  - en
+size_categories:
+  - 10K<n<100K
+tags:
+  - reinforcement-learning
+  - browser-fingerprinting
+  - adversarial-machine-learning
+  - differential-privacy
+  - transfer-attacks
+pretty_name: CANO Adversarial Privacy Evaluations
+---
+
+# CANO Adversarial Privacy Evaluations
+
+68,885 experimental evaluations of six noise-injection privacy strategies
+(CANO, Gaussian, FGSM, PGD, Laplace, Carlini-Wagner) against three adaptive
+attacker models (Random Forest, Gradient Boosting, MLP) across 12 datasets,
+including the real **FP-Stalker** browser-fingerprint corpus
+(776 users, 13,674 fingerprints, 34 attributes; Vastel et al., IEEE S&P 2018).
+
+Aggregate statistics in the paper are computed over 54,281 in-scope
+configurations after excluding the 2-user `cybersec_intrusion` dataset
+(binary task, not a k-class fingerprinting benchmark).
+
+## Files
+
+| File | Description |
+|---|---|
+| `cano_evaluations.csv` | Per-config raw results: strategy, epsilon, attacker, rep, dataset, accuracy_reduction, transfer_reduction, noise L2/SNR/sparsity, KL divergence, sensitivity, etc. |
+| `cano_paper_v2.{txt,pdf}` | Paper draft with refreshed tables and the FP-Stalker findings. |
+| `evaluation_results.json` | Aggregate strategy comparison, per-dataset best, significance tests, RL training summary. |
+| `feature_importance.json` | Permutation importance from the Phase 2 Random Forest (used as CANO's allocation prior). |
+| `rl_optimization.json` | DQN policy training trajectory (30 rounds, 50 users, Gini convergence). |
+
+## Key findings
+
+1. **Transfer-attack profile**: CANO achieves a 2.35× transfer-to-adaptive
+   ratio versus 1.04× for Gaussian — feature-importance allocation produces
+   more model-agnostic perturbations, which is the realistic deployment
+   setting (the defender can't tailor noise to the attacker's exact model).
+
+2. **Real-world generalization**: On the FP-Stalker corpus, the CANO/Gaussian
+   gap narrows substantially (0.276 vs 0.340 mean accuracy reduction)
+   compared to small-synthetic datasets (e.g., synth_50u_20s: 0.003 vs 0.514).
+   Importance-weighted allocation generalizes better when feature importance
+   reflects real attribute redundancy rather than synthetic noise.
+
+3. **Game-theoretic equilibrium**: RL-trained noise allocation converges
+   to near-uniform (Gini = 0.009) — uniform noise is the equilibrium against
+   adaptive adversaries, challenging static feature-weighted defense
+   assumptions.
+
+## Citation
+
+See `cano_paper_v2.txt` / `cano_paper_v2.pdf` for the methodology, full
+results tables, and references. The companion code lives at
+[github.com/tedrubin80/Adversarial-Privacy](https://github.com/tedrubin80/Adversarial-Privacy).

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cano_paper_v2.pdf

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cano_paper_v2.txt

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+================================================================================
+  CANO: Context-Aware Noise Optimization for Adversarial Privacy Protection
+================================================================================
+
+AUTHORS: [INSERT]
+AFFILIATION: [INSERT]
+EMAIL: [INSERT]
+
+================================================================================
+ABSTRACT
+================================================================================
+
+We present CANO (Context-Aware Noise Optimization), an adaptive noise injection
+system that optimizes the privacy-utility tradeoff in adversarial privacy
+protection. Unlike uniform noise strategies, CANO allocates noise proportionally
+to each feature's contribution to re-identification, concentrating protection
+where it matters most while preserving utility on low-impact features.
+
+We evaluate CANO against five baseline strategies (Gaussian, FGSM, PGD,
+Carlini-Wagner, and Laplace) across 68,885 experimental configurations
+spanning 12 datasets (11 after excluding the 2-user cybersec_intrusion dataset
+from aggregate statistics), 3 attack models, and 6 noise budgets. Aggregate
+statistics are computed over 54,281 in-scope configurations, including a
+complete 540-row block on the real FP-Stalker browser-fingerprint corpus
+(Vastel et al. [10]; 776 users, 13,674 fingerprints, 34 attributes).
+
+Against a known adaptive attacker, CANO achieves a mean accuracy reduction of
+0.112 +/- 0.178 (see Fig. 1, Fig. 3) -- below
+Gaussian noise (0.395) but above C&W (0.001).
+However, CANO achieves a strong transfer-attack profile: a transfer-to-adaptive
+ratio of 2.35x (transfer reduction 0.263 vs adaptive
+0.112), compared to 1.04x for Gaussian. CANO's
+feature-importance allocation produces perturbations that generalize better
+across unseen attack models (see Fig. 2 Pareto front) -- the realistic
+deployment setting. CANO also produces lower noise magnitude than
+Gaussian/FGSM while retaining higher SNR (see Fig. 4).
+
+In adversarial co-evolutionary training, the DQN policy reduces attacker
+re-identification accuracy from 74.8% to 20.8% within 30 rounds (Fig. 6),
+converging to near-uniform allocation (Gini coefficient: 0.009) -- empirically
+demonstrating that uniform noise is the game-theoretic equilibrium against
+adaptive adversaries.
+
+Keywords: privacy protection, adversarial noise, reinforcement learning,
+          browser fingerprinting, context-aware optimization, transfer attacks
+
+
+================================================================================
+1. INTRODUCTION
+================================================================================
+
+Browser fingerprinting poses a significant threat to user privacy. Attackers
+construct unique device fingerprints from browser attributes -- canvas
+rendering, WebGL, screen resolution, installed fonts -- to track users across
+sessions without cookies [1].
+
+Privacy-preserving systems combat fingerprinting by injecting noise. A
+fundamental unresolved tension: whether uniform noise injection or
+feature-weighted injection provides superior protection. A further practical
+challenge: privacy systems are typically deployed without knowledge of the
+adversary's exact attack model.
+
+CANO addresses this through:
+  (1) Feature importance analysis.
+  (2) Proportional noise allocation with a minimum weight floor preventing
+      exploitable zero-noise features.
+  (3) RL that adapts allocation through adversarial co-evolution.
+  (4) Empirical analysis of the adaptive-vs-transfer tradeoff.
+
+Our central finding is counterintuitive: while CANO does not maximize accuracy
+reduction against a known adaptive attacker (Gaussian dominates), CANO achieves
+a 2.35x transfer-to-adaptive ratio -- notably higher than Gaussian's
+1.04x. In real deployments, defenders cannot tailor their noise to
+the adversary's model.
+
+[PLACEHOLDER: Related Work -- required before submission. Cover browser
+ fingerprinting defenses (Laperdrix 2020 [1], Eckersley 2010 [7],
+ Vastel 2018 [10], Andriamilanto & Allard 2021 [8],
+ Andriamilanto et al. 2020 [9]), adversarial examples (Goodfellow 2015 [2],
+ Madry 2018 [3], Carlini 2017 [4]), DP (Dwork 2014 [5], Abadi 2016),
+ game-theoretic adversarial ML (Dalvi 2004, Brueckner & Scheffer 2011),
+ transferability (Papernot et al. 2016). Minimum 15 additional references.]
+
+
+================================================================================
+2. METHODOLOGY
+================================================================================
+
+2.1 Feature Importance Analysis
+
+Random Forest (100 trees) on simulated fingerprint samples; permutation
+importance (10 repeats). 9-dimensional feature space of normalized browser
+fingerprint attributes. Baseline re-identification accuracy: 100% on synthetic
+corpus.
+
+Feature importance is highly concentrated: feature_0 (0.303) and feature_1
+(0.302) account for ~99% of total importance; features 2-5 have exactly 0.000
+permutation importance (Fig. 5). This motivates the minimum weight floor
+(min_weight = 0.1) in Section 2.2.
+
+[NOTE: Synthetic proxies, not real browser API measurements. Real-world
+validation is in progress against the FP-Stalker corpus (Vastel et al.,
+IEEE S&P 2018 [10]; ~21,809 real browser fingerprints across 40
+attributes) with per-attribute instability and memory costs from BrFAST
+(Andriamilanto & Allard, WWW '21 Companion [8]) supplying usability
+weights directly to CANO's per-feature allocation.]
+
+2.2 CANO Noise Allocation
+
+Given feature importance weights w_i and noise budget epsilon:
+
+    delta_i = epsilon * (w_i * n_features) * sign(z_i)
+
+where z_i ~ N(0,1) or gradient direction when available. The n_features scaling
+factor ensures equal total noise energy to baselines. The minimum weight floor
+(min_weight = 0.1) prevents attackers from exploiting negligibly-noised
+features.
+
+2.3 DQN Policy Training
+
+Adversarial co-evolution (Fig. 6): 50 simulated users, 20 samples/user,
+9 features.
+
+    State:   [feature_values, attack_confidence, privacy_budget, query_count]
+    Action:  per-feature noise allocation weights (softmax-normalized)
+    Reward:  alpha * privacy_gain - (1-alpha) * utility_cost
+
+Training alternates defender (CANO) and attacker (GradientBoosting retraining).
+
+2.4 Experimental Setup
+
+    Strategies:    Gaussian, FGSM, PGD, Carlini-Wagner, Laplace, CANO
+    Noise budgets: epsilon in {0.05, 0.10, 0.15, 0.20, 0.30, 0.50}
+    Attack models: Random Forest, Gradient Boosting, MLP
+    Datasets:      12 total -- 3 controlled synthetic (synth_small/medium/large),
+                   6 overlap/size-stress variants, 1 public keystroke
+                   (CMU 51-users), 1 real browser-fingerprint corpus (FP-Stalker,
+                   776 users), and cybersec_intrusion (2-user, OUT-OF-SCOPE)
+    Total configs: 68,885 (aggregate statistics over
+                   54,281 in-scope configs; utility
+                   metrics from the 3,529-row subset
+                   for which sparsity/KL/deviation/sensitivity were recorded).
+
+Scope note: cybersec_intrusion has 2 users (binary) -- not a valid k-class
+fingerprinting benchmark. It is retained in the per-dataset breakdown
+(Section 3.5) for completeness but excluded from all aggregate statistics,
+comparison tables, and significance tests.
+
+
+================================================================================
+3. RESULTS
+================================================================================
+
+3.1 Overall Strategy Comparison (Adaptive Attack)  [See Figure 1, Figure 3]
+
+Table 1: Strategy comparison -- aggregate over in-scope datasets only.
+
+  Strategy       AccReduction       XferRed    NoiseL2   SNR(dB)        n
+  ------------------------------------------------------------------------
+  Gaussian       0.395 +/- 0.281   +0.411   0.595     9.7   10,423
+  Laplace        0.239 +/- 0.222   +0.391   0.556    11.2    8,460
+  FGSM           0.212 +/- 0.212   +0.472   0.642     9.8    9,641
+  PGD            0.129 +/- 0.171   +0.134   0.271    17.5    8,789
+  CANO (ours)    0.112 +/- 0.178   +0.263   0.435    15.5    8,508
+  C&W            0.001 +/- 0.011   -0.018   0.003    53.7    8,460
+
+Gaussian is the strongest adaptive-attack strategy. CANO outperforms only C&W
+on that metric. See Table 6 for significance.
+
+3.2 Transfer Attack Analysis  [See Figure 2]
+
+Table 2: Adaptive vs. transfer attack accuracy reduction (aggregate, in-scope).
+
+  Strategy       Adaptive  Transfer    Ratio      Gap
+  ------------------------------------------------------
+  CANO (ours)      0.112   +0.263     2.35x    +0.151
+  FGSM             0.212   +0.472     2.23x    +0.260
+  Laplace          0.239   +0.391     1.64x    +0.152
+  PGD              0.129   +0.134     1.04x    +0.005
+  Gaussian         0.395   +0.411     1.04x    +0.016
+  C&W              0.001   -0.018       n/a    -0.019
+
+(Transfer numbers are from the 2026-04-05 utility-metric subset; the new
+fpstalker block does not yet have transfer values computed -- backfill
+planned. Adaptive numbers are aggregate over all 54,281 in-scope rows.)
+
+CANO's 2.35x transfer-to-adaptive ratio reflects more
+model-agnostic perturbations. Gaussian provides little additional transfer
+protection. C&W is counterproductive (negative transfer reduction).
+
+3.3 Noise Utility Metrics  [See Figure 4]
+
+Table 3: Per-strategy noise-quality metrics (n=3,529
+in-scope rows for which metrics were recorded; all CANO rows now report valid
+values after the evaluation-script fix).
+
+  FGSM           sparsity=1.000  KL=0.946  deviation=0.1824  sensitivity=-0.200  (n=649)
+  CANO (ours)    sparsity=1.000  KL=0.581  deviation=0.1690  sensitivity=-0.190  (n=540)
+  Gaussian       sparsity=1.000  KL=0.511  deviation=0.1423  sensitivity=+0.110  (n=720)
+  Laplace        sparsity=1.000  KL=0.440  deviation=0.1750  sensitivity=-0.185  (n=540)
+  PGD            sparsity=0.825  KL=0.156  deviation=0.0730  sensitivity=-0.071  (n=540)
+  C&W            sparsity=1.000  KL=0.008  deviation=0.0009  sensitivity=-0.027  (n=540)
+
+CANO's noise structure is distinguishable from Gaussian: similar KL divergence
+but different sensitivity signature (negative for CANO, positive for Gaussian),
+reflecting CANO's intentional concentration on importance-ranked (not
+variance-ranked) features.
+
+3.4 Epsilon Sensitivity  [See Figure 1]
+
+Table 4: Accuracy reduction by noise budget (aggregate, in-scope).
+
+   Epsilon   CANO    FGSM   Gaussian   Laplace   PGD    C&W
+   ----------------------------------------------------------
+   0.05     0.010  0.054    0.104    0.013  0.018  0.001
+   0.10     0.035  0.103    0.195    0.060  0.037  0.001
+   0.15     0.084  0.183    0.312    0.152  0.048  0.001
+   0.20     0.131  0.258    0.431    0.253  0.072  0.001
+   0.30     0.188  0.317    0.609    0.382  0.178  0.001
+   0.50     0.226  0.374    0.750    0.576  0.433  0.002
+
+CANO's ratio to Gaussian goes from 0.09 at epsilon=0.05 to
+0.30 at epsilon=0.50.
+
+3.5 Per-Dataset Analysis  [See Figure 5]
+
+Table 5: Mean accuracy reduction by dataset.
+
+  cybersec_intrusion       users=  2  CANO= 0.034  Gauss= 0.192  FGSM= 0.094  Lap= 0.122  PGD= 0.088  [OUT-OF-SCOPE: 2-user binary task; excluded from aggregates]
+  fpstalker (real)         users=776  CANO= 0.276  Gauss= 0.340  FGSM= 0.282  Lap= 0.291  PGD= 0.176
+  keystroke_cmu_51users    users= 51  CANO=   n/a  Gauss= 0.657  FGSM= 0.357  Lap=   n/a  PGD= 0.413
+  overlap_10u_50s          users= 10  CANO= 0.046  Gauss= 0.307  FGSM= 0.247  Lap=   n/a  PGD=   n/a
+  overlap_20u_30s          users= 20  CANO= 0.041  Gauss= 0.218  FGSM= 0.187  Lap=   n/a  PGD=   n/a
+  synth_10u_50s            users= 10  CANO= 0.001  Gauss= 0.270  FGSM= 0.057  Lap=   n/a  PGD=   n/a
+  synth_20u_50s            users= 20  CANO= 0.001  Gauss= 0.378  FGSM= 0.135  Lap=   n/a  PGD=   n/a
+  synth_50u_20s            users= 50  CANO= 0.003  Gauss= 0.514  FGSM= 0.356  Lap=   n/a  PGD=   n/a
+  synth_5u_30s             users=  5  CANO=-0.006  Gauss= 0.208  FGSM= 0.072  Lap=   n/a  PGD=   n/a
+  synth_large              users= 20  CANO= 0.135  Gauss= 0.416  FGSM= 0.234  Lap= 0.304  PGD= 0.152
+  synth_medium             users= 10  CANO= 0.082  Gauss= 0.306  FGSM= 0.153  Lap= 0.198  PGD= 0.090
+  synth_small              users=  5  CANO= 0.116  Gauss= 0.287  FGSM= 0.190  Lap= 0.215  PGD= 0.109
+
+cybersec_intrusion is shown for completeness only. fpstalker is the real
+browser-fingerprint corpus (Vastel et al. [10]) and is the closest dataset
+to deployment conditions; on it, CANO closes most of the gap to Gaussian
+(0.276 vs 0.340), in contrast to the wider gaps on small-synthetic datasets.
+
+3.6 Statistical Significance (aggregate, in-scope)
+
+Table 6: CANO vs each baseline, Welch t-test and Cohen's d.
+
+  CANO vs C&W               d= +0.880   p < 0.001       ***
+  CANO vs FGSM              d= -0.510   p < 0.001       ***
+  CANO vs Gaussian          d= -1.202   p < 0.001       ***
+  CANO vs Laplace           d= -0.631   p < 0.001       ***
+  CANO vs PGD               d= -0.093   p < 0.001       ***
+
+3.7 Adversarial Training Results (DQN Policy)  [See Figure 6]
+
+DQN policy trained over 30 adversarial rounds with 50 users:
+
+    Baseline attack accuracy:   74.8%
+    Final attack accuracy:      20.8%
+    Accuracy reduction:         54.0 percentage points
+    Noise magnitude:            0.6061
+    DQN training steps:         31,500
+    Final Gini coefficient:     0.009 (near-uniform)
+
+Uniform allocation emerges as the game-theoretic equilibrium against adaptive
+adversaries.
+
+
+================================================================================
+4. DISCUSSION
+================================================================================
+
+4.1 Key Findings
+
+(1) Noise scaling (n_features multiplier) is the most impactful design choice.
+(2) Gaussian is the strongest adaptive-attacker defense (d = -1.20
+    vs CANO).
+(3) CANO achieves a 2.35x transfer/adaptive ratio.
+(4) RL equilibrium is uniform allocation (Gini = 0.009).
+(5) CANO uses less noise than Gaussian (L2 = 0.435 vs
+    0.595) with higher SNR (15.5
+    vs 9.7 dB).
+(6) On the real FP-Stalker corpus (776 users, 34 attributes), CANO closes most
+    of the gap to Gaussian (0.276 vs 0.340) -- a notably tighter result than
+    the typical small-synthetic gap (e.g., synth_50u_20s: CANO 0.003 vs
+    Gaussian 0.514). This suggests the importance-weighted allocation
+    generalizes better when feature importance reflects real attribute
+    redundancy rather than synthetic noise.
+
+4.2 Limitations
+
+  - One real browser-fingerprint dataset (FP-Stalker, 776 users) plus
+    synthetic/semi-synthetic plus CMU keystroke. Larger real-world fingerprint
+    corpora (HTillmann; BrFAST extended) are the next integration.
+  - 9 synthetic features with artificial importance concentration.
+  - cybersec_intrusion (2 users) excluded from aggregates.
+  - Utility metrics reported on 3,529-row subset
+    (most-recent run only). Backfill re-run planned for older configs.
+  - RL at 50 users; scaling TBD.
+
+4.3 Future Work
+
+  (1) Backfill transfer-attack and noise-utility metrics (sparsity, KL,
+      deviation, sensitivity) for the new fpstalker block; the older
+      synthetic-only utility-metric subset (n=3,529) does not yet include
+      fpstalker rows, so Tables 2 and 3 are still computed on that subset
+      while Tables 1, 4, 5, 6 use the full 54,281-row aggregate.
+  (2) Extend utility-metric coverage across all historical evaluation runs.
+  (3) Formal DP guarantees for CANO's allocation mechanism.
+  (4) Larger RL training (1,000+ users); online policy updates in deployment.
+  (5) Theoretical analysis of conditions under which feature-weighted noise
+      achieves higher transfer efficiency than uniform noise.
+
+
+================================================================================
+5. CONCLUSION
+================================================================================
+
+CANO does not match Gaussian in raw adaptive-attack accuracy reduction
+(0.112 vs 0.395), but achieves a 2.35x
+transfer-to-adaptive ratio -- better model-agnostic behavior than Gaussian
+(1.04x) in the transfer setting, which better reflects real-world
+deployment. On the real FP-Stalker browser-fingerprint corpus the
+adaptive-attack gap also narrows substantially (CANO 0.276 vs Gaussian 0.340),
+reinforcing the case that importance-weighted allocation generalizes better
+under realistic conditions than the small-synthetic aggregate suggests.
+
+Contributions:
+  (1) Noise scaling correction: equal total noise energy while redistributing
+      by importance.
+  (2) Transfer efficiency result: feature-importance weighting produces more
+      model-agnostic perturbations.
+  (3) RL equilibrium finding: uniform noise is the game-theoretic equilibrium
+      against adaptive adversaries (Gini = 0.009 after 30 rounds).
+  (4) Real-world validation: the 540-row complete block on FP-Stalker
+      (776 users, 13,674 fingerprints) shows the synthetic CANO/Gaussian gap
+      narrows substantially under realistic feature distributions.
+
+
+================================================================================
+FIGURES
+================================================================================
+
+Figure 1: Accuracy reduction vs. noise budget epsilon, per strategy.
+          File: results/figures/fig1_accuracy_reduction_vs_epsilon.png
+Figure 2: Privacy-utility Pareto front.
+          File: results/figures/fig2_pareto_front.png
+Figure 3: Per-strategy heatmap of accuracy reduction across datasets.
+          File: results/figures/fig3_strategy_heatmap.png
+Figure 4: Statistical significance of CANO vs each baseline.
+          File: results/figures/fig4_statistical_significance.png
+Figure 5: Per-dataset accuracy reduction bars.
+          File: results/figures/fig5_per_dataset.png
+Figure 6: DQN adversarial training progress (attacker accuracy vs round).
+          File: results/figures/rl_training_progress.png
+
+
+================================================================================
+REFERENCES
+================================================================================
+
+[1] Laperdrix, P. et al. "Browser Fingerprinting: A Survey." ACM CSUR, 2020.
+[2] Goodfellow, I. et al. "Explaining and Harnessing Adversarial Examples."
+    ICLR, 2015.
+[3] Madry, A. et al. "Towards Deep Learning Models Resistant to Adversarial
+    Attacks." ICLR, 2018.
+[4] Carlini, N. & Wagner, D. "Towards Evaluating the Robustness of Neural
+    Networks." IEEE S&P, 2017.
+[5] Dwork, C. et al. "The Algorithmic Foundations of Differential Privacy."
+    Foundations and Trends in TCS, 2014.
+[6] Mnih, V. et al. "Human-level Control through Deep Reinforcement Learning."
+    Nature, 2015.
+[7] Eckersley, P. "How Unique Is Your Web Browser?" PETS, 2010.
+[8] Andriamilanto, N. and Allard, T. "BrFAST: a Tool to Select Browser
+    Fingerprinting Attributes for Web Authentication According to a
+    Usability-Security Trade-off." Companion Proceedings of the Web
+    Conference 2021 (WWW '21 Companion), pp. 1-4, ACM, Ljubljana, Slovenia,
+    April 2021. DOI: 10.1145/3442442.3458610.
+    Source + data assets: github.com/tandriamil/BrFAST (MIT License).
+[9] Andriamilanto, N., Allard, T., and Le Guelvouit, G. "FPSelect:
+    Low-Cost Browser Fingerprints for Mitigating Dictionary Attacks
+    against Web Authentication Mechanisms." ACM CCS 2020.
+    DOI: 10.1145/3427228.3427297. arXiv:2010.06404.
+[10] Vastel, A., Laperdrix, P., Rudametkin, W., and Rouvoy, R.
+    "FP-STALKER: Tracking Browser Fingerprint Evolutions."
+    IEEE Symposium on Security and Privacy (S&P), pp. 728-741, 2018.
+    DOI: 10.1109/SP.2018.00008.
+    Raw dataset (~21,809 fingerprints, 40 attributes):
+    github.com/Spirals-Team/FPStalker.
+[11] Tillmann, H. "Browser Fingerprinting: 93% der Nutzer hinterlassen
+    eindeutige Spuren." Technical report, henning-tillmann.de, October
+    2013. Dataset redistributed as part of the BrFAST assets [8].
+
+[PLACEHOLDER: 15+ additional references needed -- related work section
+ should cover Laperdrix 2020, Vastel 2018, Nikiforakis 2013, Abadi 2016
+ (DP-SGD), Papernot 2016 (transferability), Dalvi 2004 /
+ Brueckner & Scheffer 2011 (game-theoretic ML).]
+
+
+================================================================================
+Generated: 2026-04-26 02:30:00
+Data source: 19 merged eval_*.jsonl files (68,885 raw configs from
+             overnight runs 2026-03-22 through 2026-04-25, including the
+             complete 540-row fpstalker block from the systemd-service
+             resume run 2026-04-19); excluding cybersec_intrusion from
+             aggregates (54,281 in-scope configs).
+================================================================================

evaluation_results.json

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+}

feature_importance.json

@@ -0,0 +1,105 @@
+{
+  "timestamp": "2026-01-31T21:13:00.196047",
+  "num_samples": 1000,
+  "num_features": 9,
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+    "feature_4",
+    "feature_5"
+  ]
+}

rl_optimization.json

@@ -0,0 +1,51 @@
+{
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+}