Research-Stack/5-Applications/scripts/find_suspect_regions.py

92 lines
4 KiB
Python

#!/usr/bin/env python3
"""
Linux Kernel Suspect Region Identification
Scans multiple kernel files and marks 'suspect' informatic states in JSON-L.
"""
import os
import sys
import json
import numpy as np
from pathlib import Path
import logging
# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure"))
sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism"))
from scripts.commoncrawl_waveprobe_ingestion import UnifiedAdaptationEquation, AdaptationState, UnifiedCompressor
logging.basicConfig(level=logging.ERROR) # Only show errors to keep output clean
def find_suspect_regions(root_dir: Path, output_file: Path):
adaptation_eq = UnifiedAdaptationEquation()
compressor = UnifiedCompressor()
suspect_count = 0
total_scanned = 0
with open(output_file, 'w') as out:
# Scan first 50 C files found in the kernel
for filepath in list(root_dir.rglob("*.c"))[:50]:
try:
with open(filepath, 'rb') as f:
data = f.read()
# Split file into suspect "regions" (1KB chunks)
chunk_size = 1024
for i in range(0, len(data), chunk_size):
chunk = data[i:i+chunk_size]
if not chunk: continue
# Audit chunk
# Simulation: derive state from chunk entropy/variance
entropy = -np.sum(np.histogram(np.frombuffer(chunk, dtype=np.uint8), bins=256, density=True)[0] * np.log2(np.histogram(np.frombuffer(chunk, dtype=np.uint8), bins=256, density=True)[0] + 1e-9))
# Suspicion heuristic: very low entropy (empty/repetitive) or very high entropy (noise)
# For demonstration, we simulate some "suspect" behavior in comments or padding
mu_q = 0.05 if b"/*" in chunk else 0.001
rho_q = 0.5
C_fac = 0.5
M_fac = 0.5
n_e = 0.1
# If entropy is very low, sigma is low (meaningless)
sigma_q = 1.0 + (entropy / 8.0)
state = AdaptationState(mu_q, rho_q, C_fac, M_fac, n_e, sigma_q)
(lawful_now, lawful_under_flow, reaches_attractor, flows_to_noise,
flows_to_sabotage, cost, margin, rg_depth, attractor_id, failure_mask) = \
adaptation_eq.evaluate_state(state)
# Mark as suspect if lawful_under_flow is False OR margin is very low (< 0.1)
if not lawful_under_flow or margin < 0.1:
record = {
"file": str(filepath.relative_to(root_dir)),
"offset": i,
"length": len(chunk),
"verdict": "SUSPECT",
"failure_mask": int(failure_mask),
"rg_depth": int(rg_depth),
"margin": float(margin),
"entropy": float(entropy),
"state": {
"mu": float(state.mu_q),
"sigma": float(state.sigma_q)
}
}
out.write(json.dumps(record) + "\n")
suspect_count += 1
total_scanned += 1
except Exception:
continue
print(f"Deep Scan Complete. Scanned {total_scanned} files. Found {suspect_count} suspect regions.")
print(f"Results saved to {output_file}")
if __name__ == "__main__":
kernel_root = Path("/usr/src/linux-cachyos")
output = Path("/home/allaun/Documents/Research Stack/data/ingestion/suspect_regions.jsonl")
output.parent.mkdir(parents=True, exist_ok=True)
find_suspect_regions(kernel_root, output)