740 lines
41 KiB
Python
740 lines
41 KiB
Python
from __future__ import annotations
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import contextlib
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import json
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import signal
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import threading
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import time
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from pathlib import Path
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from .anomaly import anomaly_summary, detect_source_anomalies
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from .baseline import BaselineStore
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from .config import ConfigStore
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from .correlation import correlate_source_ips
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from .event_context import build_event_context
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from .feedback import FeedbackStore
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from .graylog_aggregate import GraylogAggregateSource
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from .graylog_mcp import GraylogMcpClient
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from .graylog_source import GraylogStreamSource
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from .history import FieldDiscoveryStore, HistoryStore, StatusSnapshotStore
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from .incidents import IncidentStore, build_incidents
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from .data_quality import assess_data_quality
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from .llm import ollama_dashboard_assessment, ollama_profile_advice
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from .logs import local_in_failures, read_events, summarize_events, top_field_values
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from .mitigation import parse_allowlist, suggest_block_candidates
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from .policies import audit_policies, read_policies
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from .profile_suggestions import apply_profile_advice, suggest_stream_profiles
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from .recommendations import build_recommendations
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from .sequences import detect_sequences
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from .threat_intel import ThreatIntelClient, enrich_ips, is_public_ip
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from .triage import build_triage_queue
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from .stream_profiles import parse_profiles
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@contextlib.contextmanager
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def _cycle_timeout(seconds: int):
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if seconds <= 0 or threading.current_thread() is not threading.main_thread() or not hasattr(signal, "SIGALRM"):
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yield
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return
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previous_handler = signal.getsignal(signal.SIGALRM)
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previous_timer = signal.getitimer(signal.ITIMER_REAL)
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started = time.monotonic()
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def _raise_timeout(_signum, _frame):
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raise TimeoutError(f"status_cycle_timeout_{seconds}s")
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signal.signal(signal.SIGALRM, _raise_timeout)
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signal.setitimer(signal.ITIMER_REAL, seconds)
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try:
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yield
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finally:
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elapsed = time.monotonic() - started
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remaining = max(0.0, previous_timer[0] - elapsed) if previous_timer[0] else 0.0
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signal.setitimer(signal.ITIMER_REAL, remaining, previous_timer[1])
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signal.signal(signal.SIGALRM, previous_handler)
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def _stream_titles(config: dict[str, object]) -> dict[str, str]:
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return {
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str(item.get("id", "")): str(item.get("title", "") or item.get("id", ""))
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for item in config.get("graylog_streams", [])
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if isinstance(item, dict) and item.get("id")
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}
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def _public_runtime_config(config: dict[str, object]) -> dict[str, object]:
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public = {
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key: value
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for key, value in config.items()
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if key not in {"graylog_mcp_token", "abuseipdb_api_key", "virustotal_api_key"}
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}
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public["graylog_mcp_token_configured"] = bool(config.get("graylog_mcp_token"))
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public["abuseipdb_api_key_configured"] = bool(config.get("abuseipdb_api_key"))
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public["virustotal_api_key_configured"] = bool(config.get("virustotal_api_key"))
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public["enabled_streams"] = sum(
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1
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for item in config.get("graylog_streams", [])
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if isinstance(item, dict) and item.get("enabled")
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)
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return public
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def _profile_field_set(profile: object | None) -> set[str]:
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if profile is None:
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return set()
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fields = {
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str(getattr(profile, "entity_field", "") or ""),
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str(getattr(profile, "timestamp_field", "") or ""),
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}
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fields.update(str(field) for field in getattr(profile, "entity_fields", ()) if field)
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fields.update(str(field) for field in getattr(profile, "categorical_fields", ()) if field)
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fields.update(str(field) for field in getattr(profile, "numeric_fields", ()) if field)
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for relation in getattr(profile, "relationship_fields", ()):
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fields.add(str(getattr(relation, "left", "") or ""))
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fields.add(str(getattr(relation, "right", "") or ""))
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return {field for field in fields if field}
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def _suggestion_field_set(suggestion: dict[str, object]) -> set[str]:
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profile = suggestion.get("profile", {}) if isinstance(suggestion.get("profile"), dict) else {}
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fields = {
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str(profile.get("entity_field", "") or ""),
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str(profile.get("timestamp_field", "") or ""),
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}
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for key in ("entity_fields", "categorical_fields", "numeric_fields"):
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value = profile.get(key, [])
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if isinstance(value, list):
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fields.update(str(field) for field in value if field)
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relationships = profile.get("relationship_fields", [])
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if isinstance(relationships, list):
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for relation in relationships:
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if isinstance(relation, dict):
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fields.add(str(relation.get("left", "") or ""))
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fields.add(str(relation.get("right", "") or ""))
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return {field for field in fields if field}
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def _needs_profile_advisor(suggestion: dict[str, object], stream_profiles: dict[str, object]) -> bool:
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if not suggestion.get("profile_exists"):
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return True
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stream_id = str(suggestion.get("stream_id", ""))
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suggested_fields = _suggestion_field_set(suggestion)
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existing_fields = _profile_field_set(stream_profiles.get(stream_id))
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return bool(suggested_fields - existing_fields)
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def _stream_name(stream_id: str, stream_titles: dict[str, str], profile: object | None = None) -> str:
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return stream_titles.get(stream_id) or getattr(profile, "name", "") or stream_id
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def _profile_name(stream_id: str, stream_titles: dict[str, str], profile: object | None = None) -> str:
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name = str(getattr(profile, "name", "") or "").strip()
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if not name or name == stream_id:
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return f"{_stream_name(stream_id, stream_titles, profile)} profile"
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return name
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def _range_seconds(value: object) -> int:
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try:
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return max(60, int(value))
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except (TypeError, ValueError):
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return 300
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def _graylog_fields_from_result(result: dict[str, object]) -> list[dict[str, object]]:
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content = result.get("result", {}).get("content", []) if isinstance(result.get("result"), dict) else []
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text = next((item.get("text", "") for item in content if isinstance(item, dict)), "")
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if not text:
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return []
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try:
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payload = json.loads(text)
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except json.JSONDecodeError:
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return []
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fields = payload.get("fields", payload) if isinstance(payload, dict) else payload
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if isinstance(fields, dict) and isinstance(fields.get("fields"), list):
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fields = fields["fields"]
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return [item for item in fields if isinstance(item, dict)] if isinstance(fields, list) else []
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def _stream_coverage(runtime_values: dict[str, object], stream_profiles: dict[str, object], stream_status: dict[str, object], profile_readiness: list[dict[str, object]], stream_titles: dict[str, str]) -> list[dict[str, object]]:
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configured = [
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item for item in runtime_values.get("graylog_streams", [])
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if isinstance(item, dict) and item.get("id")
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]
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status_by_id = {
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str(item.get("stream_id", "")): item
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for item in stream_status.get("streams", [])
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if isinstance(item, dict) and item.get("stream_id")
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}
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readiness_by_stream: dict[str, list[dict[str, object]]] = {}
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for item in profile_readiness:
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readiness_by_stream.setdefault(str(item.get("stream_id", "")), []).append(item)
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ids = list(dict.fromkeys([str(item.get("id", "")) for item in configured] + list(stream_profiles) + list(status_by_id)))
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rows = []
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for stream_id in ids:
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profile = stream_profiles.get(stream_id)
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readiness = readiness_by_stream.get(stream_id, [])
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ready_fields = sum(1 for item in readiness if item.get("ready"))
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total_fields = len(readiness)
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status = status_by_id.get(stream_id, {})
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enabled = next((bool(item.get("enabled")) for item in configured if str(item.get("id", "")) == stream_id), False)
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events_fetched = int(status.get("events_fetched", 0) or 0)
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health = "not_enabled" if not enabled else "poll_budget_skipped" if status.get("error") == "skipped_poll_budget" else "partial_fetch" if status.get("partial") else "missing_profile" if not profile else "no_events" if events_fetched == 0 else "learning" if total_fields and ready_fields < total_fields else "ready" if total_fields else "profile_needs_fields"
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rows.append({
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"stream_id": stream_id,
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"stream_name": _stream_name(stream_id, stream_titles, profile),
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"enabled": enabled,
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"profile": _profile_name(stream_id, stream_titles, profile) if profile else "",
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"profile_ready": bool(profile),
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"entity_field": ", ".join(getattr(profile, "entity_fields", ()) or (str(getattr(profile, "entity_field", "")),)) if profile else "",
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"tracked_fields": len(getattr(profile, "categorical_fields", ())) + len(getattr(profile, "numeric_fields", ())) + len(getattr(profile, "relationship_fields", ())) if profile else 0,
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"ready_fields": ready_fields,
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"total_fields": total_fields,
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"readiness": f"{ready_fields}/{total_fields}" if total_fields else "0/0",
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"events_fetched": events_fetched,
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"aggregate_events": int(status.get("aggregate_events", 0) or 0),
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"aggregate_status": str(status.get("aggregate_status", "")),
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"aggregate_schema_properties": ", ".join(str(item) for item in status.get("aggregate_schema_properties", []) if item),
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"latest_event_time": str(status.get("latest_event_time", "")),
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"truncated": bool(status.get("truncated")),
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"partial": bool(status.get("partial")),
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"raw_error": str(status.get("error", "")),
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"aggregate_error": str(status.get("aggregate_error", "")),
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"error": str(status.get("aggregate_error", "") or status.get("error", "")),
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"health": health,
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"health_detail": str(status.get("health_detail", "")) or ("No raw events returned for this stream in the current MCP poll window." if enabled and events_fetched == 0 else ""),
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})
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return rows
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def build_status(
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log_path: str,
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*,
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policy_path: str | None = None,
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min_block_events: int = 3,
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min_block_score: int = 7,
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anomaly_limit: int = 20,
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baseline_path: str | None = None,
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config_path: str | None = None,
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history_path: str | None = None,
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incident_path: str | None = None,
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status_cache_path: str | None = None,
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) -> dict[str, object]:
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config_store = ConfigStore(config_path) if config_path else None
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config_exists = bool(config_store and config_store.path.exists())
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runtime_values = config_store.read() if config_exists and config_store else {}
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runtime_config = _public_runtime_config(runtime_values) if config_store else {}
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stream_profiles = parse_profiles(runtime_values.get("graylog_stream_profiles", []))
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stream_titles = _stream_titles(runtime_values)
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events = read_events(log_path) if Path(log_path).exists() else []
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mcp_status: dict[str, object] = {"status": "not_configured"}
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if runtime_values.get("log_source") == "graylog_mcp":
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url, token = str(runtime_values.get("graylog_mcp_url", "")), str(runtime_values.get("graylog_mcp_token", ""))
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verify_tls = bool(runtime_values.get("graylog_tls_verify", True))
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if not url or not token:
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mcp_status = {"status": "missing_configuration"}
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events = []
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else:
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try:
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configured_streams = runtime_values.get("graylog_streams", [])
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stream_configs = [item for item in configured_streams if isinstance(item, dict) and item.get("enabled") and item.get("id")]
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stream_ids = [str(item.get("id")) for item in stream_configs]
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if not stream_ids:
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legacy_stream = str(runtime_values.get("graylog_stream", "") or "")
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if legacy_stream:
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stream_configs = [{"id": legacy_stream, "title": "Graylog"}]
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else:
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mcp_status = {"status": "no_streams_enabled", "streams": [], "events_fetched": 0, "coverage_status": "no_streams_enabled"}
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events = []
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raise StopIteration
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stream_statuses = []
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events = []
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range_seconds = _range_seconds(runtime_values.get("graylog_range_seconds", 300))
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max_events_per_stream = max(1, int(runtime_values.get("graylog_max_events_per_stream", 5000) or 5000))
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raw_sample_events = max(1, int(runtime_values.get("graylog_raw_sample_events", 5000) or 5000))
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mcp_call_timeout = max(1, int(runtime_values.get("graylog_mcp_call_timeout_seconds", 8) or 8))
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mcp_poll_timeout = max(60, int(runtime_values.get("graylog_mcp_poll_timeout_seconds", 120) or 120))
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fetch_mode = str(runtime_values.get("graylog_fetch_mode", "auto") or "auto")
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use_aggregate = fetch_mode == "aggregate" or (fetch_mode == "auto" and max_events_per_stream > raw_sample_events)
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aggregate_events_total = 0
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poll_started_monotonic = time.monotonic()
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poll_deadline = poll_started_monotonic + mcp_poll_timeout
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client = GraylogMcpClient(url, token, timeout=mcp_call_timeout, verify_tls=verify_tls)
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probe_status = client.probe()
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discovery_store = FieldDiscoveryStore(history_path) if history_path else None
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catalog_fields_total = 0
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def budget_exceeded() -> bool:
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return time.monotonic() >= poll_deadline
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def skipped_status(stream_id: str, stream_name: str) -> dict[str, object]:
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return {
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"stream_id": stream_id,
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"stream_name": stream_name,
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"source": "graylog_mcp",
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"events_fetched": 0,
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"aggregate_events": 0,
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"pages": 0,
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"partial": True,
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"error": "skipped_poll_budget",
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"truncated": False,
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"latest_event_time": "",
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"raw_sample_limit": min(raw_sample_events, 10_000) if use_aggregate else max_events_per_stream,
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"health_detail": "Skipped because the MCP poll time budget was reached before this stream.",
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}
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for stream_config in stream_configs:
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stream_id = str(stream_config["id"])
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stream_name = str(stream_config.get("title", "") or stream_titles.get(stream_id) or stream_id)
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if budget_exceeded():
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stream_statuses.append(skipped_status(stream_id, stream_name))
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continue
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profile = stream_profiles.get(stream_id)
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profile_fields = (
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str(getattr(profile, "entity_field", "")),
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*tuple(str(field) for field in getattr(profile, "entity_fields", ())),
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str(getattr(profile, "timestamp_field", "")),
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*tuple(str(field) for field in getattr(profile, "categorical_fields", ())),
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*tuple(str(field) for field in getattr(profile, "numeric_fields", ())),
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*tuple(str(getattr(relation, "left", "")) for relation in getattr(profile, "relationship_fields", ())),
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*tuple(str(getattr(relation, "right", "")) for relation in getattr(profile, "relationship_fields", ())),
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) if profile else ()
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if discovery_store and not budget_exceeded():
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try:
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catalog_fields_total += discovery_store.ingest_catalog(stream_id, stream_name, _graylog_fields_from_result(client.call_tool("list_fields", {"streams": [stream_id]})))
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except RuntimeError:
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pass
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aggregate_status: dict[str, object] = {}
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if use_aggregate and not budget_exceeded():
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aggregate_status = GraylogAggregateSource(client, stream_id, str(runtime_values.get("graylog_query", "*"))).fetch_count(range_seconds=range_seconds, probe_status=probe_status, deadline_monotonic=poll_deadline)
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aggregate_events_total += int(aggregate_status.get("aggregate_events", 0) or 0)
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raw_limit = min(raw_sample_events, 10_000) if use_aggregate else max_events_per_stream
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if budget_exceeded():
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stream_statuses.append({**skipped_status(stream_id, stream_name), **aggregate_status})
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continue
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stream_events, stream_status = GraylogStreamSource(client, stream_id, str(runtime_values.get("graylog_query", "*")), str(runtime_values.get("graylog_field_mapping", "")), stream_name, profile_fields).fetch(max_events=raw_limit, range_seconds=range_seconds, probe_status=probe_status, deadline_monotonic=poll_deadline)
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events.extend(stream_events)
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stream_statuses.append({"stream_id": stream_id, "stream_name": stream_name, **aggregate_status, **stream_status, "raw_sample_limit": raw_limit})
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sample_limited_streams = [item for item in stream_statuses if item.get("truncated") and use_aggregate]
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truncated_streams = [item for item in stream_statuses if item.get("truncated") and not use_aggregate]
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partial_streams = [item for item in stream_statuses if item.get("partial")]
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skipped_streams = [item for item in stream_statuses if item.get("error") == "skipped_poll_budget"]
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aggregate_errors = [item for item in stream_statuses if item.get("aggregate_status") == "error"]
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warnings = []
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if skipped_streams:
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warnings.append(f"{len(skipped_streams)} stream(s) skipped because the MCP poll time budget was reached.")
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if aggregate_errors:
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warnings.append(f"{len(aggregate_errors)} stream(s) returned aggregate MCP errors.")
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if partial_streams:
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warnings.append(f"{len(partial_streams)} stream(s) returned a partial MCP fetch; Graylog likely timed out or rejected a large paged query.")
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if truncated_streams:
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warnings.append(f"{len(truncated_streams)} stream(s) hit max_events_per_stream; high EPS means the analysis window is only partially sampled.")
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mcp_status = {
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"status": "partial" if partial_streams or aggregate_errors else "connected",
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"streams": stream_statuses,
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"events_fetched": len(events),
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"raw_events_fetched": len(events),
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"aggregate_events": aggregate_events_total,
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"poll_completed_at": int(time.time()),
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"poll_duration_seconds": round(time.monotonic() - poll_started_monotonic, 2),
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"fetch_mode": "aggregate" if use_aggregate else "raw",
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"range_seconds": range_seconds,
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"max_events_per_stream": max_events_per_stream,
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"raw_sample_events": raw_sample_events,
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"call_timeout_seconds": mcp_call_timeout,
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"poll_timeout_seconds": mcp_poll_timeout,
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"partial_streams": len(partial_streams),
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"skipped_streams": len(skipped_streams),
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"sample_limited_streams": len(sample_limited_streams),
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"truncated_streams": len(truncated_streams),
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"aggregate_error_streams": len(aggregate_errors),
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"catalog_fields": catalog_fields_total,
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"coverage_status": "partial" if partial_streams else "truncated" if truncated_streams else "complete_window",
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"coverage_warning": " ".join(warnings),
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}
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except StopIteration:
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pass
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except RuntimeError as exc:
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mcp_status = {"status": "error", "error": str(exc)}
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events = []
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baseline = BaselineStore(baseline_path) if baseline_path else None
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profiles = baseline.profiles({event.src_ip for event in events if event.src_ip}) if baseline else {}
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baseline_training_days = int(runtime_values.get("baseline_training_days", 7) or 7)
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field_deviations = baseline.profile_deviations(events, stream_profiles, min_training_days=baseline_training_days) if baseline else {}
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sequence_findings = detect_sequences(events)
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for entity, findings in sequence_findings.items():
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field_deviations.setdefault(entity, []).extend(findings)
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for deviations in field_deviations.values():
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for deviation in deviations:
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stream_id = str(deviation.get("stream_id", ""))
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profile = stream_profiles.get(stream_id)
|
|
name = _stream_name(stream_id, stream_titles, profile)
|
|
deviation["stream_name"] = name
|
|
deviation["stream_title"] = name
|
|
deviation["profile_name"] = _profile_name(stream_id, stream_titles, profile)
|
|
deviation["sample_events"] = [
|
|
{"stream": name, **sample} if isinstance(sample, dict) and not sample.get("stream") else sample
|
|
for sample in deviation.get("sample_events", [])
|
|
]
|
|
feedback = FeedbackStore().entries()
|
|
for entity, deviations in field_deviations.items():
|
|
for deviation in deviations:
|
|
match = next((
|
|
item for item in feedback
|
|
if item.get("entity") == entity
|
|
and item.get("stream_id") == deviation.get("stream_id")
|
|
and item.get("field") == deviation.get("field")
|
|
and (not item.get("value") or item.get("value") == deviation.get("value", ""))
|
|
), None)
|
|
if match:
|
|
deviation["feedback"] = match["status"]
|
|
if match["status"] in {"false_positive", "expected"}:
|
|
deviation["score"] = 0
|
|
anomalies = detect_source_anomalies(events, limit=anomaly_limit, baselines=profiles, field_deviations=field_deviations)
|
|
baseline_events = baseline.ingest(events) if baseline else 0
|
|
profile_baseline_fields = baseline.ingest_profile_fields(events, stream_profiles) if baseline else 0
|
|
baseline_maintenance = (
|
|
baseline.maintenance(
|
|
retention_days=int(runtime_values.get("baseline_retention_days", 14) or 14),
|
|
value_retention_days=int(runtime_values.get("baseline_value_retention_days", 7) or 7),
|
|
max_values_per_field=int(runtime_values.get("baseline_max_values_per_field", 2000) or 2000),
|
|
)
|
|
if baseline
|
|
else {}
|
|
)
|
|
profile_readiness = baseline.profile_readiness(stream_profiles, min_training_days=baseline_training_days) if baseline else []
|
|
profile_readiness = [
|
|
{
|
|
**item,
|
|
"profile_name": _profile_name(str(item.get("stream_id", "")), stream_titles, stream_profiles.get(str(item.get("stream_id", "")))),
|
|
"stream_name": _stream_name(str(item.get("stream_id", "")), stream_titles, stream_profiles.get(str(item.get("stream_id", "")))),
|
|
}
|
|
for item in profile_readiness
|
|
]
|
|
stream_coverage = _stream_coverage(runtime_values, stream_profiles, mcp_status, profile_readiness, stream_titles)
|
|
discovery_cache_events = []
|
|
discovered_profile_fields = 0
|
|
if history_path:
|
|
discovery_store = FieldDiscoveryStore(history_path)
|
|
discovered_profile_fields = discovery_store.ingest(events)
|
|
discovery_cache_events = discovery_store.synthetic_events()
|
|
profile_suggestions = suggest_stream_profiles([*discovery_cache_events, *events], existing_profiles=stream_profiles)
|
|
profile_advisor_status = {"enabled": bool(runtime_values.get("profile_advisor_enabled")), "status": "disabled"}
|
|
advisor_candidates = [item for item in profile_suggestions if _needs_profile_advisor(item, stream_profiles)]
|
|
if runtime_values.get("profile_advisor_enabled") and profile_suggestions and not advisor_candidates:
|
|
profile_advisor_status = {"enabled": True, "status": "skipped_no_profile_changes", "reason": "No missing profiles or newly discovered profile fields need advisor review."}
|
|
elif runtime_values.get("profile_advisor_enabled") and profile_suggestions:
|
|
try:
|
|
advice = ollama_profile_advice(
|
|
advisor_candidates[:10],
|
|
model=str(runtime_values.get("profile_advisor_model", "") or "qwen3:8b"),
|
|
timeout=int(runtime_values.get("profile_advisor_timeout", 120) or 120),
|
|
)
|
|
profile_suggestions = apply_profile_advice(profile_suggestions, advice)
|
|
profile_advisor_status = {"enabled": True, "status": "ok" if advice else "empty", "profiles_returned": len(advice), "model": str(runtime_values.get("profile_advisor_model", "") or "qwen3:8b")}
|
|
except Exception as exc:
|
|
profile_advisor_status = {"enabled": True, "status": "error", "error": str(exc), "model": str(runtime_values.get("profile_advisor_model", "") or "qwen3:8b")}
|
|
for suggestion in profile_suggestions:
|
|
suggestion.setdefault("profile_advisor", {"status": "heuristic", "error": str(exc)})
|
|
intel_ips = sorted(
|
|
{
|
|
ip
|
|
for event in events
|
|
for ip in (event.src_ip, event.dst_ip)
|
|
if is_public_ip(ip)
|
|
}
|
|
)
|
|
threat_enabled = bool(runtime_values.get("threat_intel_enabled")) if runtime_values else None
|
|
reputation = enrich_ips(intel_ips, limit=25, enabled=threat_enabled, config=runtime_values)
|
|
threat_intel_status = ThreatIntelClient(
|
|
enabled=threat_enabled,
|
|
provider=str(runtime_values.get("threat_intel_provider", "auto")),
|
|
abuseipdb_key=str(runtime_values.get("abuseipdb_api_key", "") or "") or None,
|
|
virustotal_key=str(runtime_values.get("virustotal_api_key", "") or "") or None,
|
|
daily_limit=int(runtime_values.get("threat_intel_daily_limit", 100) or 100),
|
|
ttl_seconds=int(runtime_values.get("threat_intel_ttl_seconds", 604800) or 604800),
|
|
error_ttl_seconds=int(runtime_values.get("threat_intel_error_ttl_seconds", 3600) or 3600),
|
|
abuseipdb_max_age_days=int(runtime_values.get("abuseipdb_max_age_days", 90) or 90),
|
|
).status()
|
|
recommendations = build_recommendations(events, anomalies, reputation)
|
|
correlations = correlate_source_ips(events)
|
|
incidents = IncidentStore(incident_path or "state/signalscope-incidents.json").apply(build_incidents(anomalies, field_deviations, correlations))
|
|
triage_queue = build_triage_queue(incidents, field_deviations, correlations, recommendations)
|
|
block_candidates = suggest_block_candidates(
|
|
events,
|
|
min_events=min_block_events,
|
|
min_score=min_block_score,
|
|
allowlist=parse_allowlist(),
|
|
)
|
|
|
|
policy_findings: list[dict[str, str | None]] = []
|
|
policy_error: str | None = None
|
|
if policy_path and Path(policy_path).exists():
|
|
try:
|
|
policy_findings = [finding.__dict__ for finding in audit_policies(read_policies(policy_path))]
|
|
except Exception as exc:
|
|
policy_error = str(exc)
|
|
|
|
summary = summarize_events(events)
|
|
if isinstance(mcp_status, dict) and int(mcp_status.get("aggregate_events", 0) or 0) > summary.get("total", 0):
|
|
summary["total"] = int(mcp_status.get("aggregate_events", 0) or 0)
|
|
summary["raw_sample_total"] = len(events)
|
|
summary["aggregate_backed"] = True
|
|
status = {
|
|
"status_schema": 2,
|
|
"stale": False,
|
|
"generated_at": int(time.time()),
|
|
"log_path": log_path,
|
|
"policy_path": policy_path,
|
|
"summary": summary,
|
|
"anomaly_summary": anomaly_summary(anomalies),
|
|
"baseline": {"enabled": bool(baseline), "sources_ready": len(profiles), "training_days": baseline_training_days, "new_events_recorded": baseline_events, "profile_fields_recorded": profile_baseline_fields, "discovery_fields_recorded": discovered_profile_fields, "discovery_cache_events": len(discovery_cache_events), "maintenance": baseline_maintenance, "size_bytes": baseline_maintenance.get("size_bytes", 0) if isinstance(baseline_maintenance, dict) else 0},
|
|
"capabilities": {"threat_intel": threat_intel_status, "graylog_mcp": mcp_status, "profile_advisor": profile_advisor_status},
|
|
"configuration": runtime_config,
|
|
"stream_profiles": [{"stream_id": item.stream_id, "name": _profile_name(item.stream_id, stream_titles, item), "stream_name": _stream_name(item.stream_id, stream_titles, item), "entity_field": item.entity_field, "entity_fields": list(item.entity_fields), "timestamp_field": item.timestamp_field, "categorical_fields": list(item.categorical_fields), "numeric_fields": list(item.numeric_fields), "detectors": item.detectors, "field_weights": item.field_weights, "relationship_fields": [{"left": relation.left, "right": relation.right, "name": relation.name} for relation in item.relationship_fields]} for item in stream_profiles.values()],
|
|
"stream_coverage": stream_coverage,
|
|
"profile_suggestions": profile_suggestions,
|
|
"profile_readiness": profile_readiness,
|
|
"diagnostics": {
|
|
"top_source_ips": top_field_values(events, "srcip", limit=10),
|
|
"top_destination_ips": top_field_values(events, "dstip", limit=10),
|
|
"top_policy_ids": top_field_values(events, "policyid", limit=10),
|
|
"top_destination_ports": top_field_values(events, "dstport", limit=10),
|
|
"top_source_ports": top_field_values(events, "srcport", limit=10),
|
|
"top_services": top_field_values(events, "service", limit=10),
|
|
"top_actions": top_field_values(events, "action", limit=10),
|
|
"top_subtypes": top_field_values(events, "subtype", limit=10),
|
|
"local_in_failures": local_in_failures(events, limit=10),
|
|
},
|
|
"event_context": build_event_context(events),
|
|
"field_deviations": field_deviations,
|
|
"triage_queue": triage_queue,
|
|
"sequence_findings": sequence_findings,
|
|
"feedback": feedback,
|
|
"cross_source_correlations": correlations,
|
|
"incidents": incidents,
|
|
"data_quality": assess_data_quality(events, mcp_status),
|
|
"status_cache": {"served_from_cache": False, "reason": ""},
|
|
"anomalies": [
|
|
{
|
|
"subject": finding.subject,
|
|
"score": finding.score,
|
|
"severity": finding.severity,
|
|
"confidence": finding.confidence,
|
|
"reasons": finding.reasons,
|
|
"evidence": finding.evidence,
|
|
}
|
|
for finding in anomalies
|
|
],
|
|
"recommendations": [
|
|
{
|
|
"subject": item.subject,
|
|
"score": item.score,
|
|
"severity": item.severity,
|
|
"title": item.title,
|
|
"recommendation": item.recommendation,
|
|
"reasons": item.reasons,
|
|
"related_policy_ids": item.related_policy_ids,
|
|
"related_services": item.related_services,
|
|
}
|
|
for item in recommendations
|
|
],
|
|
"reputation": reputation,
|
|
"block_candidates": [
|
|
{"src_ip": candidate.src_ip, "score": candidate.score, "reasons": candidate.reasons}
|
|
for candidate in block_candidates
|
|
],
|
|
"policy_findings": policy_findings,
|
|
"policy_error": policy_error,
|
|
}
|
|
if history_path:
|
|
history = HistoryStore(history_path)
|
|
history.record(status)
|
|
status["history"] = history.recent()
|
|
if status_cache_path and not (isinstance(mcp_status, dict) and mcp_status.get("status") == "error"):
|
|
StatusSnapshotStore(status_cache_path).save("last_good", status)
|
|
return status
|
|
|
|
|
|
def cached_status_with_error(cache_path: str, error_status: dict[str, object]) -> dict[str, object] | None:
|
|
cached = StatusSnapshotStore(cache_path).load("last_good")
|
|
if not cached:
|
|
return None
|
|
for row in cached.get("stream_coverage", []) if isinstance(cached.get("stream_coverage"), list) else []:
|
|
if isinstance(row, dict):
|
|
row.setdefault("raw_error", "")
|
|
row.setdefault("aggregate_error", "")
|
|
row.setdefault("error", row.get("aggregate_error") or row.get("raw_error") or "")
|
|
if row.get("aggregate_status") == "error" and not row.get("aggregate_error") and row.get("error"):
|
|
row["aggregate_error"] = str(row.get("error", ""))
|
|
if row.get("partial") and not row.get("raw_error") and row.get("error"):
|
|
row["raw_error"] = str(row.get("error", ""))
|
|
cached["status_schema"] = 2
|
|
cached["generated_at"] = int(time.time())
|
|
cached["stale"] = True
|
|
cached["stale_reason"] = "live_mcp_error"
|
|
capabilities = cached.setdefault("capabilities", {})
|
|
if isinstance(capabilities, dict):
|
|
capabilities["graylog_mcp"] = error_status
|
|
cached.setdefault("status_cache", {})
|
|
if isinstance(cached["status_cache"], dict):
|
|
cached["status_cache"].update({"served_from_cache": True, "reason": "live_mcp_error"})
|
|
return cached
|
|
|
|
|
|
def add_llm_assessment(status: dict[str, object], *, previous: str | None = None, model: str | None = None, timeout: int | None = None) -> None:
|
|
try:
|
|
status["llm_assessment"] = {
|
|
"enabled": True,
|
|
"status": "ok",
|
|
"generated_at": int(time.time()),
|
|
"text": ollama_dashboard_assessment(status, model=model, timeout=timeout),
|
|
}
|
|
except Exception as exc:
|
|
status["llm_assessment"] = {
|
|
"enabled": True,
|
|
"status": "error",
|
|
"generated_at": int(time.time()),
|
|
"error": str(exc),
|
|
"text": previous or "",
|
|
}
|
|
|
|
|
|
def write_status(status: dict[str, object], output: str) -> None:
|
|
output_path = Path(output)
|
|
output_path.parent.mkdir(parents=True, exist_ok=True)
|
|
tmp_path = output_path.with_suffix(f"{output_path.suffix}.tmp")
|
|
tmp_path.write_text(json.dumps(status, indent=2, sort_keys=True), encoding="utf-8")
|
|
tmp_path.replace(output_path)
|
|
|
|
|
|
def write_refreshing_status(output: str, *, cache_path: str | None = None, call_timeout_seconds: int = 0, poll_timeout_seconds: int = 0, runtime_values: dict[str, object] | None = None) -> None:
|
|
output_path = Path(output)
|
|
now = int(time.time())
|
|
try:
|
|
current = json.loads(output_path.read_text(encoding="utf-8")) if output_path.exists() else {}
|
|
except (json.JSONDecodeError, OSError):
|
|
current = {}
|
|
if not isinstance(current, dict):
|
|
current = {}
|
|
used_cache = False
|
|
if cache_path:
|
|
cached = StatusSnapshotStore(cache_path).load("last_good")
|
|
if cached:
|
|
current = cached
|
|
used_cache = True
|
|
previous_mcp = current.get("capabilities", {}).get("graylog_mcp", {}) if isinstance(current.get("capabilities"), dict) else {}
|
|
previous_completed_at = previous_mcp.get("poll_completed_at", current.get("generated_at", 0)) if isinstance(previous_mcp, dict) else current.get("generated_at", 0)
|
|
current.setdefault("status_schema", 2)
|
|
current["generated_at"] = now
|
|
current["stale"] = False
|
|
current["stale_reason"] = ""
|
|
if runtime_values is not None:
|
|
stream_profiles = parse_profiles(runtime_values.get("graylog_stream_profiles", []))
|
|
current["configuration"] = _public_runtime_config(runtime_values)
|
|
current["stream_coverage"] = _stream_coverage(runtime_values, stream_profiles, {"streams": []}, [], _stream_titles(runtime_values))
|
|
capabilities = current.setdefault("capabilities", {})
|
|
if isinstance(capabilities, dict):
|
|
previous_events = previous_mcp.get("events_fetched", 0) if isinstance(previous_mcp, dict) else 0
|
|
previous_raw_events = previous_mcp.get("raw_events_fetched", 0) if isinstance(previous_mcp, dict) else 0
|
|
previous_aggregate_events = previous_mcp.get("aggregate_events", 0) if isinstance(previous_mcp, dict) else 0
|
|
previous_fetch_mode = previous_mcp.get("fetch_mode", "") if isinstance(previous_mcp, dict) else ""
|
|
previous_coverage_status = previous_mcp.get("coverage_status", "") if isinstance(previous_mcp, dict) else ""
|
|
capabilities["graylog_mcp"] = {
|
|
"status": "refreshing",
|
|
"previous_status": previous_mcp.get("status", "") if isinstance(previous_mcp, dict) else "",
|
|
"previous_error": previous_mcp.get("error", "") if isinstance(previous_mcp, dict) else "",
|
|
"previous_events_fetched": previous_events,
|
|
"previous_raw_events_fetched": previous_raw_events,
|
|
"previous_aggregate_events": previous_aggregate_events,
|
|
"previous_fetch_mode": previous_fetch_mode,
|
|
"previous_coverage_status": previous_coverage_status,
|
|
"events_fetched": previous_events,
|
|
"raw_events_fetched": previous_raw_events,
|
|
"aggregate_events": previous_aggregate_events,
|
|
"poll_started_at": now,
|
|
"previous_poll_completed_at": previous_completed_at,
|
|
"fetch_mode": previous_fetch_mode,
|
|
"coverage_status": previous_coverage_status or "refreshing",
|
|
"call_timeout_seconds": call_timeout_seconds or previous_mcp.get("call_timeout_seconds", 0),
|
|
"poll_timeout_seconds": poll_timeout_seconds or previous_mcp.get("poll_timeout_seconds", 0),
|
|
}
|
|
current["status_cache"] = {"served_from_cache": used_cache, "reason": "refreshing"}
|
|
write_status(current, output)
|
|
|
|
|
|
def monitor_loop(
|
|
log_path: str,
|
|
output: str,
|
|
*,
|
|
policy_path: str | None = None,
|
|
interval: int = 10,
|
|
anomaly_limit: int = 20,
|
|
llm: bool = False,
|
|
llm_interval: int = 300,
|
|
llm_model: str | None = None,
|
|
llm_timeout: int | None = None,
|
|
baseline_path: str | None = None,
|
|
config_path: str | None = None,
|
|
history_path: str | None = None,
|
|
status_cache_path: str | None = None,
|
|
) -> None:
|
|
print(f"Monitoring {log_path}", flush=True)
|
|
print(f"Writing status to {output}", flush=True)
|
|
last_llm_at = 0
|
|
last_llm_text: str | None = None
|
|
while True:
|
|
runtime = ConfigStore(config_path).read() if config_path and Path(config_path).exists() else {}
|
|
effective_llm = bool(runtime.get("llm_enabled")) if runtime else llm
|
|
effective_model = str(runtime.get("llm_model") or llm_model or "")
|
|
mcp_call_timeout = max(1, int(runtime.get("graylog_mcp_call_timeout_seconds", 8) or 8))
|
|
mcp_poll_timeout = max(60, int(runtime.get("graylog_mcp_poll_timeout_seconds", 120) or 120))
|
|
if runtime.get("log_source") == "graylog_mcp":
|
|
write_refreshing_status(output, cache_path=status_cache_path, call_timeout_seconds=mcp_call_timeout, poll_timeout_seconds=mcp_poll_timeout, runtime_values=runtime)
|
|
try:
|
|
status_timeout = mcp_poll_timeout + max(30, mcp_call_timeout * 2)
|
|
with _cycle_timeout(status_timeout if runtime.get("log_source") == "graylog_mcp" else 0):
|
|
status = build_status(
|
|
log_path, policy_path=policy_path, anomaly_limit=anomaly_limit,
|
|
baseline_path=baseline_path, config_path=config_path, history_path=history_path,
|
|
status_cache_path=status_cache_path,
|
|
)
|
|
except Exception as exc:
|
|
error_status = {"status": "error", "error": f"monitor_error: {exc}", "call_timeout_seconds": mcp_call_timeout, "poll_timeout_seconds": mcp_poll_timeout}
|
|
status = cached_status_with_error(status_cache_path, error_status) if status_cache_path else None
|
|
if not status:
|
|
status = {
|
|
"status_schema": 2,
|
|
"generated_at": int(time.time()),
|
|
"stale": True,
|
|
"stale_reason": "monitor_error",
|
|
"capabilities": {"graylog_mcp": error_status},
|
|
"summary": {"total": 0},
|
|
"status_cache": {"served_from_cache": False, "reason": "monitor_error"},
|
|
}
|
|
mcp = status.get("capabilities", {}).get("graylog_mcp", {}) if isinstance(status.get("capabilities"), dict) else {}
|
|
if status_cache_path and isinstance(mcp, dict) and mcp.get("status") == "error":
|
|
cached = cached_status_with_error(status_cache_path, mcp)
|
|
if cached:
|
|
status = cached
|
|
if effective_llm:
|
|
now = int(time.time())
|
|
if now - last_llm_at >= llm_interval:
|
|
add_llm_assessment(status, previous=last_llm_text, model=effective_model or None, timeout=llm_timeout)
|
|
assessment = status.get("llm_assessment", {})
|
|
if isinstance(assessment, dict):
|
|
last_llm_text = str(assessment.get("text", "") or last_llm_text or "")
|
|
last_llm_at = now
|
|
else:
|
|
status["llm_assessment"] = {
|
|
"enabled": True,
|
|
"status": "cached",
|
|
"generated_at": last_llm_at,
|
|
"text": last_llm_text or "",
|
|
}
|
|
else:
|
|
status["llm_assessment"] = {"enabled": False, "status": "disabled", "text": ""}
|
|
if status_cache_path and isinstance(mcp, dict) and mcp.get("status") not in {"error", "refreshing"}:
|
|
StatusSnapshotStore(status_cache_path).save("last_good", status)
|
|
write_status(status, output)
|
|
time.sleep(interval)
|