File size: 3,803 Bytes
124ea58 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | """
Preprocessing pipeline for logs, JSON alerts, and raw text.
Prepares input for narrative generation and entity extraction.
"""
import json
import re
from typing import Any
def parse_input(raw: str) -> dict[str, Any]:
"""
Parse user input (text or JSON) into a normalized structure.
Returns:
dict with keys: type, content, lines, parsed (if JSON)
"""
raw = raw.strip() or ""
if not raw:
return {"type": "empty", "content": "", "lines": []}
# Try JSON first
try:
parsed = json.loads(raw)
if isinstance(parsed, list):
lines = [json.dumps(item) if isinstance(item, dict) else str(item) for item in parsed]
elif isinstance(parsed, dict):
lines = [raw]
else:
lines = [str(parsed)]
return {
"type": "json",
"content": raw,
"parsed": parsed,
"lines": lines,
}
except json.JSONDecodeError:
pass
# Treat as plain text / log lines
lines = [ln.strip() for ln in raw.splitlines() if ln.strip()]
return {
"type": "text",
"content": raw,
"lines": lines,
}
def extract_iocs(text: str) -> list[str]:
"""Extract potential IOCs (IPs, domains, hashes, URLs) using regex."""
iocs: list[str] = []
# IPv4
for m in re.finditer(r"\b(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\b", text):
iocs.append(m.group())
# MD5
for m in re.finditer(r"\b[a-fA-F0-9]{32}\b", text):
iocs.append(m.group())
# SHA256
for m in re.finditer(r"\b[a-fA-F0-9]{64}\b", text):
iocs.append(m.group())
# Domains (simple)
for m in re.finditer(r"\b(?:[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?\.)+[a-zA-Z]{2,}\b", text):
iocs.append(m.group())
# CVE IDs
for m in re.finditer(r"CVE-\d{4}-\d{4,}", text, re.I):
iocs.append(m.group())
# MITRE TTP (T####)
for m in re.finditer(r"T\d{4}(?:\.\d{3})?", text):
iocs.append(m.group())
return list(dict.fromkeys(iocs))
def parse_alert(alert: dict[str, Any]) -> str:
"""Convert alert dict into a flat text string for model input."""
parts: list[str] = []
for k, v in alert.items():
if isinstance(v, (dict, list)):
parts.extend(flatten_json(v, k))
else:
parts.append(f"{k}={v}")
return " | ".join(parts)
def preprocess_for_model(raw: str) -> str:
"""
Full preprocessing pipeline: parse logs/JSON/alerts -> single text string.
Use as model input.
"""
parsed = parse_input(raw)
if parsed["type"] == "empty":
return ""
lines = parsed["lines"]
if parsed["type"] == "json":
if isinstance(parsed.get("parsed"), list):
texts = []
for item in parsed["parsed"]:
if isinstance(item, dict):
texts.append(parse_alert(item))
else:
texts.append(str(item))
return "\n".join(texts)
elif isinstance(parsed.get("parsed"), dict):
return parse_alert(parsed["parsed"])
return parsed["content"]
def flatten_json(obj: Any, prefix: str = "") -> list[str]:
"""Flatten JSON object into key=value log-like strings."""
out: list[str] = []
if isinstance(obj, dict):
for k, v in obj.items():
key = f"{prefix}.{k}" if prefix else k
out.extend(flatten_json(v, key))
elif isinstance(obj, list):
for i, v in enumerate(obj):
key = f"{prefix}[{i}]" if prefix else f"[{i}]"
out.extend(flatten_json(v, key))
else:
out.append(f"{prefix}={obj}" if prefix else str(obj))
return out
|