mirror of
https://github.com/marcredhat/SIEM-toolkit-patched
synced 2026-06-08 12:33:51 +00:00
Mick
c5a4f796a0
Key changes: - Unlabelled event banner: shows count only after Sample Events is clicked; uses broad SDL filter expression; time window synced to sync-days dropdown - Parser Quality: new "Attributes Missing" subsection listing all parsers without dataSource.name regardless of event volume - Coverage map: filter buttons (All / Complete Parser / Attributes Missing); stat card renamed to "Incomplete Parser"; stub count excluded from sync when no active sources - Sync All button: runs SDL parser sync → library sync → live sources sync in sequence - Reset now clears ActiveSource table and resets unlabelled count cache - run_powerquery: configurable max_count param (default 1000, 50M for count queries) - _DS_NAME_RE: supports both quoted and unquoted dataSource.name keys in parser files - Full modern UI redesign: slate palette, gradient cards, ring borders, pill nav, colored stat accents - Updated 7 tracked parser files synced from SDL Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
509 lines
19 KiB
Python
509 lines
19 KiB
Python
from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from datetime import datetime, timedelta
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from services import s1_client
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import os
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import re
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router = APIRouter()
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@router.get("/parsers")
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def list_parser_files():
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"""List parser filenames available under /app/parsers/ for the Test Runner."""
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parsers_dir = "/app/parsers"
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try:
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names = sorted(
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e.name for e in os.scandir(parsers_dir)
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if e.is_file() and not e.name.startswith(".")
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)
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except FileNotFoundError:
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names = []
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return {"parsers": names, "count": len(names)}
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def _date_range_hours(hours: int) -> tuple[str, str]:
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now = datetime.utcnow()
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return (
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(now - timedelta(hours=hours)).strftime("%Y-%m-%dT%H:%M:%S.000Z"),
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now.strftime("%Y-%m-%dT%H:%M:%S.000Z"),
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)
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# ---------------------------------------------------------------------------
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# Models
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# ---------------------------------------------------------------------------
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class SampleEventsRequest(BaseModel):
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source: str
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limit: int = 20
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hours: int = 1
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filter_mode: str = "broad" # reserved for future use
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class FieldPopulationRequest(BaseModel):
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source: str
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hours: int = 24
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fields: list[str] = [
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"src.ip",
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"src.port",
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"dst.ip",
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"dst.port",
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"user.name",
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"event.type",
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"src.process.name",
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"src.process.cmdline",
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"tgt.file.path",
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"network.direction",
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"dataSource.name",
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]
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class TestParserRequest(BaseModel):
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parser_name: str
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log_line: str
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _flatten_dict(d: dict, prefix: str = "", out: dict | None = None) -> dict:
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"""Recursively flatten a nested dict into dotted keys."""
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if out is None:
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out = {}
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if not isinstance(d, dict):
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return out
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for k, v in d.items():
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key = f"{prefix}.{k}" if prefix else k
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if isinstance(v, dict):
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_flatten_dict(v, key, out)
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else:
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out[key] = v
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return out
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def _flatten_event(event: dict) -> dict:
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"""Return a flat field→value dict from a PowerQuery result row.
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If the row only carries a JSON-stringified payload in `message` (i.e. the
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parser wasn't applied at query time), parse and flatten it inline so the
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UI can measure field population accurately. The original raw `message`
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is preserved under its own key.
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"""
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if not isinstance(event, dict):
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return {}
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flat = dict(event)
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msg = flat.get("message")
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if isinstance(msg, str) and msg.startswith("{") and msg.endswith("}"):
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try:
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parsed = __import__("json").loads(msg)
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if isinstance(parsed, dict):
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flat.update(_flatten_dict(parsed))
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except Exception:
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pass
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return flat
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def _extract_format_strings(content: str) -> list[str]:
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"""
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Extract SDL format string values from augmented-JSON parser content.
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Handles both:
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- quoted keys: "format": "..." (valid JSON)
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- unquoted keys: format: "..." (SDL augmented-JSON)
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Skips commented-out lines (// ...).
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"""
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pattern = re.compile(r'(?<!//)\"?format\"?\s*:\s*"((?:[^"\\]|\\.)*)"')
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results = []
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for line in content.splitlines():
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stripped = line.strip()
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if stripped.startswith("//"):
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continue
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results.extend(pattern.findall(line))
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return results
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def _sdl_format_to_regex(fmt: str) -> tuple[re.Pattern, dict[str, str]]:
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"""
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Convert an SDL format string to a compiled Python regex.
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SDL format strings may start with '.*,' to absorb a syslog header. When
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used with re.search that prefix is redundant AND harmful (it forces a comma
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before the first named field, which won't exist when the log starts with
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the field directly). We strip the leading '.*,' so re.search can anchor
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to the first real field at any position in the line.
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Internal '.*' wildcards (field separators for skipped fields) are kept as
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non-greedy '.*?' so they don't consume adjacent named-field values.
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Returns (compiled_pattern, py_group_to_sdl_field).
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Raises re.error if the resulting pattern cannot be compiled.
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"""
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# Strip leading/trailing .* wildcards — re.search handles positioning
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fmt = re.sub(r'^(\.\*,?)+', '', fmt)
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fmt = re.sub(r'(,?\.\*)+$', '', fmt)
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# Split on $...$ tokens
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token_pattern = re.compile(r'\$([^$]+)\$')
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parts = token_pattern.split(fmt)
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# parts alternates: literal, token, literal, token, ...
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regex_parts: list[str] = []
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py_group_to_sdl: dict[str, str] = {}
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seen_groups: dict[str, int] = {}
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def _escape_literal(s: str) -> str:
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"""Escape literal text but keep internal .* as non-greedy wildcards."""
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segments = re.split(r'(\.\*)', s)
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return ''.join(r'.*?' if seg == '.*' else re.escape(seg) for seg in segments)
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for i, part in enumerate(parts):
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if i % 2 == 0:
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# Literal text (possibly containing .* wildcards)
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regex_parts.append(_escape_literal(part))
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else:
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# Token: either "field.name=PATTERN" or just "field.name"
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if '=' in part:
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field_name, pattern = part.split('=', 1)
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else:
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field_name = part
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# Default: match any non-comma chars (SDL CSV fields)
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pattern = r'[^,]*'
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# Build a valid Python named-group identifier
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safe = re.sub(r'[.\-]', '_', field_name)
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if safe in seen_groups:
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seen_groups[safe] += 1
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safe = f"{safe}_{seen_groups[safe]}"
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else:
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seen_groups[safe] = 0
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py_group_to_sdl[safe] = field_name
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regex_parts.append(f'(?P<{safe}>{pattern})')
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compiled = re.compile(''.join(regex_parts), re.IGNORECASE | re.DOTALL)
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return compiled, py_group_to_sdl
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# ---------------------------------------------------------------------------
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# Endpoints
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# ---------------------------------------------------------------------------
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@router.post("/sample-unlabelled")
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async def sample_unlabelled(req: SampleEventsRequest):
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"""Return a sample of events that have no dataSource.name — these need parsers.
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Also runs a count query so the caller can update the banner with the real total.
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"""
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import asyncio
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from routers import coverage as _coverage
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filter_expr = "!(dataSource.name = *) !(source = 'scalyr')"
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from_dt, to_dt = _date_range_hours(req.hours)
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sample_result, count_result = await asyncio.gather(
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s1_client.run_powerquery(f"{filter_expr} | limit {req.limit}", from_dt, to_dt),
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s1_client.run_powerquery(f"{filter_expr} | group events=count()", from_dt, to_dt, max_count=50_000_000),
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)
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rows = sample_result if isinstance(sample_result, list) else (sample_result.get("rows") or sample_result.get("events") or [])
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events = [_flatten_event(row) for row in rows]
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non_empty_keys: set = set()
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for ev in events:
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for k, v in ev.items():
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if v is not None and v != "" and v != "null":
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non_empty_keys.add(k)
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events = [{k: v for k, v in ev.items() if k in non_empty_keys} for ev in events]
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count_rows = count_result.get("events", []) if isinstance(count_result, dict) else []
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total = count_rows[0].get("events", 0) if count_rows else 0
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_coverage._unlabelled_event_count = total
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return {
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"events": events,
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"count": len(events),
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"total": total,
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"hours": req.hours,
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"columns_seen": sorted(non_empty_keys),
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}
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@router.post("/sample-events")
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async def sample_events(req: SampleEventsRequest):
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"""Return a sample of raw events from a given data source."""
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query = f'| filter dataSource.name = "{req.source}" | limit {req.limit}'
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from_dt, to_dt = _date_range_hours(req.hours)
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result = await s1_client.run_powerquery(query, from_dt, to_dt)
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rows = result if isinstance(result, list) else (result.get("rows") or result.get("events") or [])
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events = [_flatten_event(row) for row in rows]
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return {
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"source": req.source,
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"events": events,
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"count": len(events),
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"hours": req.hours,
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}
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@router.post("/field-population")
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async def field_population(req: FieldPopulationRequest):
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"""
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Analyse how consistently each requested field is populated across a sample
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of events from a data source.
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"""
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query = f'| filter dataSource.name = "{req.source}" | limit 500'
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from_dt, to_dt = _date_range_hours(req.hours)
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result = await s1_client.run_powerquery(query, from_dt, to_dt)
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rows = result if isinstance(result, list) else (result.get("rows") or result.get("events") or [])
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events = [_flatten_event(row) for row in rows]
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if not events:
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return {
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"source": req.source,
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"total_sampled": 0,
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"hours": req.hours,
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"fields": [],
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"fields_seen_in_sample": [],
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"message": f"No events found for source '{req.source}' in the last {req.hours} hours.",
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}
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total = len(events)
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_empty_scalars = {None, "", "null"}
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def _is_empty(val):
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"""Return True if the value counts as unpopulated."""
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if val is None:
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return True
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if isinstance(val, list):
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return len(val) == 0
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if isinstance(val, dict):
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return len(val) == 0
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return val in _empty_scalars
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# Collect all field names seen across the sample (useful for surfacing what IS there)
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all_seen_fields = sorted({k for ev in events for k in ev})
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all_seen_fields_set = set(all_seen_fields)
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field_stats = []
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for field in req.fields:
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# Skip fields that don't appear anywhere in the sample
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if field not in all_seen_fields_set:
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continue
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populated = sum(1 for ev in events if not _is_empty(ev.get(field)))
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rate = round((populated / total) * 100, 1)
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field_stats.append({
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"field": field,
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"populated": populated,
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"total": total,
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"rate": rate,
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})
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# Sort descending by rate (best coverage first)
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field_stats.sort(key=lambda x: x["rate"], reverse=True)
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return {
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"source": req.source,
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"total_sampled": total,
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"hours": req.hours,
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"fields": field_stats,
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"fields_seen_in_sample": all_seen_fields,
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}
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@router.post("/test-parser")
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async def test_parser(req: TestParserRequest):
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"""
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Test a parser against a raw log line by extracting and matching SDL format
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strings found in the parser file.
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"""
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parser_path = f"/app/parsers/{req.parser_name}"
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try:
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with open(parser_path, "r", encoding="utf-8") as fh:
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content = fh.read()
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except FileNotFoundError:
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raise HTTPException(status_code=404, detail=f"Parser file not found: {req.parser_name}")
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except OSError as exc:
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raise HTTPException(status_code=500, detail=f"Could not read parser file: {exc}")
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format_strings = _extract_format_strings(content)
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# ── JSON auto-extract path ──────────────────────────────────────────────
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# SDL parsers that use `$=json{parse=json}$` (or any format containing
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# `parse=json`) auto-extract every top-level JSON key as an attribute.
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# The regex-based path can't model that — handle it explicitly so users
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# can test JSON-shaped logs against JSON-mode parsers.
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log_input = req.log_line.strip()
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# Only enter JSON mode if the log content actually looks like JSON.
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# Don't force it based on the parser type alone — a JSON-capable parser
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# should still fall through to regex matching for non-JSON inputs.
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is_json_mode = log_input.startswith("{") or log_input.startswith("[")
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if is_json_mode:
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import json as _json
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# Support multi-line input (one JSON object per line, or a JSON array)
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lines = [ln for ln in (l.strip() for l in log_input.splitlines()) if ln]
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payloads: list[dict] = []
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parse_errors: list[str] = []
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# Single line: try direct parse; if it's a JSON array, expand.
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if len(lines) == 1:
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try:
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obj = _json.loads(lines[0])
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except Exception as e:
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return {
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"parser_name": req.parser_name,
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"matched": False,
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"message": f"Parser expects JSON but log line could not be parsed as JSON: {e}",
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"fields": [],
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}
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if isinstance(obj, list):
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payloads = [x for x in obj if isinstance(x, dict)]
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elif isinstance(obj, dict):
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payloads = [obj]
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else:
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return {
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"parser_name": req.parser_name,
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"matched": False,
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"message": "Parser expects a JSON object (got scalar).",
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"fields": [],
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}
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else:
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# Multi-line: one JSON object per line (NDJSON)
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for i, ln in enumerate(lines, 1):
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try:
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obj = _json.loads(ln)
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if isinstance(obj, dict):
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payloads.append(obj)
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else:
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parse_errors.append(f"line {i}: not a JSON object")
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except Exception as e:
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parse_errors.append(f"line {i}: {e}")
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if not payloads:
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return {
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"parser_name": req.parser_name,
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"matched": False,
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"message": "No valid JSON objects found. " + " | ".join(parse_errors[:3]),
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"fields": [],
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}
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# Use the first payload for the detail table; report totals.
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payload = payloads[0]
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extracted = _flatten_dict(payload)
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# SDL's parse=json puts all keys into unmapped.* namespace first, then
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# rewrites map unmapped.X -> ocsf.field. Mirror that so rewrites fire.
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unmapped_aliases = {f"unmapped.{k}": v for k, v in extracted.items()}
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extracted_with_unmapped = {**extracted, **unmapped_aliases}
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# Apply lightweight rewrites if present (input/output/match/replace blocks).
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# We only handle simple literal/regex matches with $0 or string replacements;
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# this is best-effort, intended for quick visual verification.
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rewrites_applied = []
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# Handle both quoted keys ("input":) and unquoted keys (input:)
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rewrite_re = re.compile(
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r'\{\s*"?input"?\s*:\s*"([^"]+)"\s*,\s*"?output"?\s*:\s*"([^"]+)"\s*,\s*"?match"?\s*:\s*"((?:[^"\\]|\\.)*)"\s*,\s*"?replace"?\s*:\s*"((?:[^"\\]|\\.)*)"\s*\}',
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re.DOTALL,
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)
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derived: dict[str, str] = {}
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for m in rewrite_re.finditer(content):
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in_field, out_field, match_pat, replace_val = m.group(1), m.group(2), m.group(3), m.group(4)
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src_val = extracted_with_unmapped.get(in_field)
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if src_val is None:
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continue
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try:
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m2 = re.search(match_pat, str(src_val))
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except re.error:
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continue
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if not m2:
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continue
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# SDL uses $0 for whole match, $1.. for groups. Translate to Python
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# \g<0>, \g<1>, ... so re.sub doesn't read \0 as a null byte.
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def _to_py_backref(s: str) -> str:
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return re.sub(r"\$(\d+)", lambda mm: f"\\g<{mm.group(1)}>", s)
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try:
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val = re.sub(match_pat, _to_py_backref(replace_val), str(src_val), count=1)
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except re.error:
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val = replace_val
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derived[out_field] = val
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rewrites_applied.append({
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"input": in_field, "input_value": src_val,
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"output": out_field, "matched_on": match_pat, "result": val,
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})
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fields = (
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[{"field": k, "value": v, "source": "json-extract"} for k, v in sorted(extracted.items())]
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+ [{"field": k, "value": v, "source": "rewrite"} for k, v in sorted(derived.items())]
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)
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return {
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"parser_name": req.parser_name,
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"matched": True,
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"mode": "json",
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"format_matched": "$=json{parse=json}$",
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"fields": fields,
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"rewrites_applied": rewrites_applied,
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"extracted_count": len(extracted),
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"derived_count": len(derived),
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"payload_count": len(payloads),
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"parse_errors": parse_errors,
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"showing_payload": 1,
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}
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# ── Regex format-string path ─────────────────────────────────────────────
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def _try_prefix_match(compiled: re.Pattern, py_to_sdl: dict, log_line: str):
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"""
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Try the full pattern; if it doesn't match, progressively shorten from
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the right (group by group) until we get a match. This handles logs
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that don't include all the trailing optional fields the parser defines.
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Returns (match, truncated) or (None, False).
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"""
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m = compiled.search(log_line)
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if m:
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return m, False
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# Shorten pattern by removing trailing named groups one at a time
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p = compiled.pattern
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# Find all (?P<name>...) group end positions (right to left)
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|
group_ends = [m2.end() for m2 in re.finditer(r'\(\?P<[^>]+>[^)]*\)', p)]
|
|
for end in reversed(group_ends[1:]): # keep at least 1 group
|
|
try:
|
|
shorter = re.compile(p[:end], re.IGNORECASE | re.DOTALL)
|
|
m2 = shorter.search(log_line)
|
|
if m2:
|
|
return m2, True
|
|
except re.error:
|
|
continue
|
|
return None, False
|
|
|
|
for fmt in format_strings:
|
|
try:
|
|
compiled, py_to_sdl = _sdl_format_to_regex(fmt)
|
|
except re.error:
|
|
continue
|
|
|
|
match, truncated = _try_prefix_match(compiled, py_to_sdl, req.log_line)
|
|
if match:
|
|
fields = [
|
|
{"field": py_to_sdl.get(group, group), "value": value}
|
|
for group, value in match.groupdict().items()
|
|
if value is not None and value != ""
|
|
]
|
|
return {
|
|
"parser_name": req.parser_name,
|
|
"matched": True,
|
|
"mode": "regex",
|
|
"format_matched": fmt[:120] + ("…" if len(fmt) > 120 else ""),
|
|
"fields": fields,
|
|
"note": "Partial match — log has fewer fields than the full parser format" if truncated else None,
|
|
}
|
|
|
|
return {
|
|
"parser_name": req.parser_name,
|
|
"matched": False,
|
|
"message": "No format pattern matched. Check that the log includes the log-type keyword (e.g. TRAFFIC, THREAT) and enough comma-separated fields.",
|
|
"fields": [],
|
|
}
|