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holland202
sentinel-batadal-validation
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REPRODUCED.md
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SENTINEL x BATADAL — Public Benchmark Validation
Vincit Omnia Veritas — Truth Conquers All.
Left panel y-axis is tick recall (attack ticks flagged / total attack ticks); the axis label reads "episode recall" and is wrong -- it will be corrected when the figure is regenerated. Episode counts are in the table below.
Independent validation of SENTINEL's detection method — two-window<br>Jensen-Shannon divergence change detection on per-channel sensor<br>histograms — against the public BATADAL water-distribution attack<br>benchmark (Taormina et al. 2018). Part of Sovereign Evolution:<br>edge-native critical-infrastructure defense, built and run entirely<br>on-device (Android/Termux, Python + NumPy, no cloud).
Not the proprietary SENTINEL v12: this is a faithful reference<br>implementation plus evaluation harness. class Detector is the seam<br>where the production detector drops in. Thresholds auto-calibrate from<br>clean data (99.5th percentile) — no proprietary constants anywhere.
Detection results (live runs, test set: 2,089 records, 7 attacks)
Profile<br>samples/bin<br>Episodes<br>Tick recall<br>F1
Conservative (w=100,b=12)<br>8.33<br>2/7<br>20.9%<br>0.251
Balanced (w=60,b=16)<br>3.75<br>4/7<br>15.2%<br>0.225
Aggressive (w=30,b=16)<br>1.88<br>3/7<br>1.2%<br>0.023
Definitions. Episodes = attack episodes with at least one flagged tick (of 7). Tick recall = TP/(TP+FN) over labeled attack ticks. These measure different things, so a profile can detect more episodes at lower tick recall.
The histogram under-sampling failure mode is real and reproduced on<br>external labeled data: at 1.88 samples/bin, recall collapses to ~1%.
Attribution results (vs published per-attack ground truth)
Config<br>Exact target in top-5<br>Attack-area in top-5
Point (w=30,b=12)<br>4/7<br>5/7
Area (w=48,b=16)<br>3/7<br>7/7
Two honest operating points: the wide window localizes every attack to<br>a physically-relevant channel; the tight window pins the exact sensor<br>more often. Attacks 9/12 replay L_T2 to SCADA, so coupled-channel<br>localization is the correct outcome there. Caveats: reference detector<br>(a baseline the tuned v12 should beat); exact-sensor localization at<br>hourly granularity is inherently limited — published literature reports<br>the same constraint.
Reproduce
pip install numpy --break-system-packages<br>bash fetch_batadal.sh<br>python3 sentinel_batadal.py
Data (c) its authors, not redistributed here — cite Taormina et al. 2018,<br>J. Water Resour. Plann. Manage. 144(8).
License
Research use with attribution — see LICENSE.md. Commercial licensing:<br>c.holland.arch@proton.me
Chad Edward Holland (@holland202) · Sovereign Evolution · Oklahoma, USA
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