A Coding Implementation to Get well Hidden Malware IOCs with FLARE-FLOSS Past Traditional Strings Evaluation

banner("STEP 6 — IOC searching within the deobfuscated strings")
PATTERNS = [
   ("URL",          re.compile(r"https?://[^s"<>]+")),
   ("IP",           re.compile(r"b(?:d{1,3}.){3}d{1,3}b")),
   ("PE/script",    re.compile(r"[A-Za-z0-9_]+.(?:exe|dll|sys|ps1|bat)b", re.I)),
   ("Win32 API",    re.compile(r"b(?:Reg(?:Open|Set|Create|Delete)Key(?:Ex)?A?|VirtualAlloc(?:Ex)?|CreateRemoteThread|WinExec|LoadLibraryA?|GetProcAddress|InternetOpenA?)b")),
   ("Registry",     re.compile(r"SOFTWARE\?[A-Za-z0-9_\]+", re.I)),
   ("Base64-like",  re.compile(r"b[A-Za-z0-9+/]{24,}={0,2}b")),
]
hits = []
for variety, objects in buckets.objects():
   for e in objects:
       s = e.get("string","")
       for label, pat in PATTERNS:
           if pat.search(s): hits.append((variety, label, s))


if hits:
   print(f"{'BUCKET':<10}{'IOC':<14}STRING")
   print("-"*72)
   for variety,lbl,s in hits[:40]:
       print(f"{variety:<10}{lbl:<14}{s[:80]}")
   print(f"n→ {len(hits)} IOC hits complete. Be aware: most are contained in the 'decoded' bucket")
   print("  — these can be invisible to plain `strings`!")
else:
   print("(no IOC sample matches)")


banner("STEP 7 — Visualize string-type counts and size distribution")
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4.5))


labels = checklist(buckets); counts = [len(v) for v in buckets.values()]
bars = ax1.bar(labels, counts, colour=["#5fa8d3","#62b6cb","#cae9ff","#ff7b7b"])
ax1.set_title("FLOSS strings by kind"); ax1.set_ylabel("rely")
for b,n in zip(bars,counts): ax1.textual content(b.get_x()+b.get_width()/2, n, str(n), ha="heart", va="backside")


for variety, objects in buckets.objects():
   lens = [len(e.get("string","")) for e in items]
   if lens: ax2.hist(lens, bins=30, alpha=0.55, label=f"{variety} (n={len(lens)})")
ax2.set_title("String-length distribution"); ax2.set_xlabel("characters")
ax2.set_ylabel("frequency (log)"); ax2.set_yscale("log"); ax2.legend()
plt.tight_layout(); plt.savefig("floss_summary.png", dpi=110); plt.present()


print("n✓ Tutorial full.")
print(f"   Artifacts: {WORK/'pattern.exe'}, {WORK/'floss.json'}, {WORK/'floss_summary.png'}")