Added Reverse-Engineering Toolkit
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186
Reverse-Engineering CAN-Bus/trace_batch_analyzer.py
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186
Reverse-Engineering CAN-Bus/trace_batch_analyzer.py
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#!/usr/bin/env python3
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import re
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import sys
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import argparse
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from pathlib import Path
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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LOG_PATTERN = re.compile(r"(\d+)\s+(TX|RX)\s+0x([0-9A-Fa-f]+)\s+\d+\s+((?:[0-9A-Fa-f]{2}\s+)+)")
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def parse_trace(path: Path, rx_only=False) -> pd.DataFrame:
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rows = []
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with open(path, "r", errors="ignore") as f:
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for line in f:
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m = LOG_PATTERN.match(line)
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if not m:
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continue
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ts = int(m.group(1))
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dr = m.group(2)
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if rx_only and dr != "RX":
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continue
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cid = int(m.group(3), 16)
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data = [int(x, 16) for x in m.group(4).split() if x.strip()]
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rows.append((ts, dr, cid, data))
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df = pd.DataFrame(rows, columns=["ts","dir","id","data"])
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if df.empty:
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return df
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df["time_s"] = (df["ts"] - df["ts"].min())/1000.0
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return df
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def be16(a,b): return (a<<8)|b
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def le16(a,b): return a | (b<<8)
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def analyze_one_trace(df: pd.DataFrame, scale=1.0, offs=0.0, rmin=None, rmax=None):
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"""Return stats for all 8-bit bytes and all adjacent 16-bit pairs (LE/BE)."""
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stats = []
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# 8-bit
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for i in range(8):
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vals = [d[i] for d in df["data"] if len(d)>i]
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if not vals: continue
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arr = np.array(vals, dtype=float)
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phys = arr*scale + offs
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hit = np.ones_like(phys, dtype=bool)
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if rmin is not None: hit &= (phys>=rmin)
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if rmax is not None: hit &= (phys<=rmax)
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stats.append({
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"type":"byte8","slot":str(i),
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"n":len(arr),
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"min":float(arr.min()),"max":float(arr.max()),"var":float(arr.var()),
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"hit_ratio": float(np.count_nonzero(hit))/len(hit) if len(hit)>0 else 0.0,
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"min_phys": float(phys.min()), "max_phys": float(phys.max())
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})
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# 16-bit
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pairs = [(i,i+1) for i in range(7)]
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for i,j in pairs:
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# LE
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vals = [le16(d[i],d[j]) for d in df["data"] if len(d)>j]
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if vals:
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arr = np.array(vals, dtype=float); phys = arr*scale + offs
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hit = np.ones_like(phys, dtype=bool)
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if rmin is not None: hit &= (phys>=rmin)
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if rmax is not None: hit &= (phys<=rmax)
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stats.append({
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"type":"le16","slot":f"{i}-{j}",
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"n":len(arr),
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"min":float(arr.min()),"max":float(arr.max()),"var":float(arr.var()),
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"hit_ratio": float(np.count_nonzero(hit))/len(hit) if len(hit)>0 else 0.0,
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"min_phys": float(phys.min()), "max_phys": float(phys.max())
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})
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# BE
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vals = [be16(d[i],d[j]) for d in df["data"] if len(d)>j]
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if vals:
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arr = np.array(vals, dtype=float); phys = arr*scale + offs
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hit = np.ones_like(phys, dtype=bool)
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if rmin is not None: hit &= (phys>=rmin)
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if rmax is not None: hit &= (phys<=rmax)
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stats.append({
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"type":"be16","slot":f"{i}-{j}",
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"n":len(arr),
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"min":float(arr.min()),"max":float(arr.max()),"var":float(arr.var()),
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"hit_ratio": float(np.count_nonzero(hit))/len(hit) if len(hit)>0 else 0.0,
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"min_phys": float(phys.min()), "max_phys": float(phys.max())
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})
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return pd.DataFrame(stats)
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def plot_one_trace(df: pd.DataFrame, outdir: Path, prefix: str):
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outdir.mkdir(parents=True, exist_ok=True)
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# 8-bit plots
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for i in range(8):
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times, series = [], []
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for t,d in zip(df["time_s"], df["data"]):
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if len(d)>i:
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times.append(t); series.append(d[i])
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if not series: continue
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import matplotlib.pyplot as plt
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plt.figure(figsize=(10,4))
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plt.plot(times, series, marker=".", linestyle="-")
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plt.xlabel("Zeit (s)"); plt.ylabel(f"Byte[{i}] (8-bit)")
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plt.title(f"{prefix} – 8-bit Byte {i}")
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plt.grid(True); plt.tight_layout()
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plt.savefig(outdir / f"{prefix}_byte{i}.png", dpi=150); plt.close()
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# 16-bit plots (LE/BE)
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pairs = [(i,i+1) for i in range(7)]
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for i,j in pairs:
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times, series = [], []
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for t,d in zip(df["time_s"], df["data"]):
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if len(d)>j: times.append(t); series.append(le16(d[i],d[j]))
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if series:
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import matplotlib.pyplot as plt
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plt.figure(figsize=(10,4))
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plt.plot(times, series, marker=".", linestyle="-")
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plt.xlabel("Zeit (s)"); plt.ylabel(f"LE16 @{i}-{j}")
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plt.title(f"{prefix} – LE16 {i}-{j}")
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plt.grid(True); plt.tight_layout()
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plt.savefig(outdir / f"{prefix}_le16_{i}-{j}.png", dpi=150); plt.close()
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times, series = [], []
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for t,d in zip(df["time_s"], df["data"]):
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if len(d)>j: times.append(t); series.append(be16(d[i],d[j]))
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if series:
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import matplotlib.pyplot as plt
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plt.figure(figsize=(10,4))
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plt.plot(times, series, marker=".", linestyle="-")
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plt.xlabel("Zeit (s)"); plt.ylabel(f"BE16 @{i}-{j}")
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plt.title(f"{prefix} – BE16 {i}-{j}")
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plt.grid(True); plt.tight_layout()
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plt.savefig(outdir / f"{prefix}_be16_{i}-{j}.png", dpi=150); plt.close()
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def main():
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ap = argparse.ArgumentParser(description="Batch analyze per-ID traces and rank 8/16-bit combinations")
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ap.add_argument("--traces-dir", required=True, help="Directory containing *.trace files")
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ap.add_argument("--outdir", required=True, help="Output directory for analysis results")
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ap.add_argument("--rx-only", action="store_true", help="Use RX frames only")
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ap.add_argument("--plots", action="store_true", help="Also generate plots for each trace")
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ap.add_argument("--scale", type=float, default=1.0, help="phys = raw*scale + offset")
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ap.add_argument("--offset", type=float, default=0.0, help="phys = raw*scale + offset")
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ap.add_argument("--range-min", type=float, default=None, help="physical min (after scale/offset)")
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ap.add_argument("--range-max", type=float, default=None, help="physical max (after scale/offset)")
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ap.add_argument("--top", type=int, default=8, help="Export top combos per trace to summary")
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args = ap.parse_args()
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tdir = Path(args.traces_dir)
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outdir = Path(args.outdir); outdir.mkdir(parents=True, exist_ok=True)
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traces = sorted([p for p in tdir.glob("*.trace")])
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if not traces:
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print("No .trace files found.", file=sys.stderr)
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sys.exit(2)
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global_rows = []
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for tr in traces:
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df = parse_trace(tr, rx_only=args.rx_only)
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if df.empty:
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continue
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stats = analyze_one_trace(df, args.scale, args.offset, args.range_min, args.range_max)
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# Ranking: primarily by hit_ratio (if range given), else by variance; break ties by var then n
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if args.range_min is not None or args.range_max is not None:
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stats = stats.sort_values(["hit_ratio","var","n"], ascending=[False, False, False])
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else:
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stats = stats.sort_values(["var","n"], ascending=[False, False])
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# write per-trace csv
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per_csv = outdir / f"{tr.stem}_combostats.csv"
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stats.to_csv(per_csv, index=False)
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# append top rows with trace id hint
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stem = tr.stem # e.g., 0x208_log1
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for _, row in stats.head(args.top).iterrows():
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r = row.to_dict()
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r["trace"] = stem
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global_rows.append(r)
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# plots (optional) into a subdir per trace
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if args.plots:
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plot_dir = outdir / f"{tr.stem}_plots"
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plot_one_trace(df, plot_dir, prefix=tr.stem)
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# global summary
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if global_rows:
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gdf = pd.DataFrame(global_rows)
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gdf.to_csv(outdir / "summary_top_combinations.csv", index=False)
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print(f"Global summary written: {outdir/'summary_top_combinations.csv'}")
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print(f"Processed {len(traces)} trace files. Results at: {outdir}")
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if __name__ == "__main__":
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main()
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