206 lines
12 KiB
Python
206 lines
12 KiB
Python
from __future__ import annotations
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import hashlib
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import sqlite3
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import time
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from collections import Counter, defaultdict
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from pathlib import Path
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from statistics import mean, pstdev
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from .logs import THREAT_ACTIONS, is_utm_event
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from .models import LogEvent
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def _number(value: str | None) -> int:
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try:
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return int(float(value or 0))
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except ValueError:
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return 0
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class BaselineStore:
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"""Persistent five-minute behavior baseline, implemented with stdlib SQLite."""
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def __init__(self, path: str, *, bucket_seconds: int = 300) -> None:
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self.path = Path(path)
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self.path.parent.mkdir(parents=True, exist_ok=True)
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self.bucket_seconds = bucket_seconds
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with self._connect() as connection:
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connection.executescript(
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"""
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create table if not exists seen_events (fingerprint text primary key);
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create table if not exists source_buckets (
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source_ip text not null, bucket_start integer not null,
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events integer not null, bytes integer not null, hitcount integer not null,
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denies integer not null, utm integer not null,
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primary key (source_ip, bucket_start)
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);
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create table if not exists source_values (
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source_ip text not null, kind text not null, value text not null,
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seen_count integer not null, primary key (source_ip, kind, value)
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);
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create table if not exists profile_buckets (
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stream_id text not null, entity text not null, field text not null, bucket_start integer not null,
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events integer not null, numeric_sum real not null, numeric_sum_squares real not null,
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primary key (stream_id, entity, field, bucket_start)
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);
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create table if not exists profile_values (
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stream_id text not null, entity text not null, field text not null, value text not null,
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seen_count integer not null, primary key (stream_id, entity, field, value)
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);
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"""
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)
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def _connect(self) -> sqlite3.Connection:
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return sqlite3.connect(self.path)
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def ingest(self, events: list[LogEvent], *, observed_at: int | None = None) -> int:
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observed_at = observed_at or int(time.time())
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pending: dict[tuple[str, int], list[int]] = defaultdict(lambda: [0, 0, 0, 0, 0])
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inserted = 0
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with self._connect() as connection:
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for event in events:
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if not event.src_ip:
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continue
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fingerprint = hashlib.sha256(event.raw.encode("utf-8", errors="replace")).hexdigest()
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if connection.execute("insert or ignore into seen_events values (?)", (fingerprint,)).rowcount != 1:
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continue
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bucket = observed_at - (observed_at % self.bucket_seconds)
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values = pending[(event.src_ip, bucket)]
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values[0] += 1
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values[1] += _number(event.fields.get("sentbyte")) + _number(event.fields.get("rcvdbyte"))
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values[2] += _number(event.fields.get("hitcount"))
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values[3] += int(event.action in THREAT_ACTIONS)
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values[4] += int(is_utm_event(event))
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for kind, value in (("destination", event.dst_ip), ("destination_port", event.fields.get("dstport"))):
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if value:
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connection.execute(
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"""insert into source_values values (?, ?, ?, 1)
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on conflict(source_ip, kind, value) do update set seen_count=seen_count+1""",
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(event.src_ip, kind, value),
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)
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inserted += 1
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for (source_ip, bucket), values in pending.items():
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connection.execute(
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"""insert into source_buckets values (?, ?, ?, ?, ?, ?, ?)
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on conflict(source_ip, bucket_start) do update set
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events=events+excluded.events, bytes=bytes+excluded.bytes,
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hitcount=hitcount+excluded.hitcount, denies=denies+excluded.denies, utm=utm+excluded.utm""",
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(source_ip, bucket, *values),
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)
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return inserted
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def ingest_profile_fields(self, events: list[LogEvent], profiles: dict[str, object], *, observed_at: int | None = None) -> int:
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observed_at = observed_at or int(time.time())
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bucket = observed_at - (observed_at % self.bucket_seconds)
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pending: dict[tuple[str, str, str], list[float]] = defaultdict(lambda: [0, 0.0, 0.0])
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pending_values: Counter[tuple[str, str, str, str]] = Counter()
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for event in events:
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stream_id = event.fields.get("fgai_stream_id", "")
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profile = profiles.get(stream_id)
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if not profile:
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continue
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entity_field = str(getattr(profile, "entity_field", "")).lower()
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entity = event.fields.get(entity_field)
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if not entity:
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continue
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fields = [*getattr(profile, "categorical_fields", ()), *getattr(profile, "numeric_fields", ())]
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numeric = {str(field).lower() for field in getattr(profile, "numeric_fields", ())}
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for field in fields:
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key = (stream_id, entity, str(field).lower())
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value = _number(event.fields.get(key[2])) if key[2] in numeric else 0
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pending[key][0] += 1
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pending[key][1] += value
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pending[key][2] += value * value
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if key[2] not in numeric:
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raw_value = event.fields.get(key[2])
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if raw_value:
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pending_values[(*key, raw_value)] += 1
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with self._connect() as connection:
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for (stream_id, entity, field), values in pending.items():
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connection.execute("""insert into profile_buckets values (?, ?, ?, ?, ?, ?, ?)
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on conflict(stream_id, entity, field, bucket_start) do update set events=events+excluded.events,numeric_sum=numeric_sum+excluded.numeric_sum,numeric_sum_squares=numeric_sum_squares+excluded.numeric_sum_squares""", (stream_id, entity, field, bucket, *values))
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for key, count in pending_values.items():
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connection.execute("""insert into profile_values values (?, ?, ?, ?, ?)
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on conflict(stream_id, entity, field, value) do update set seen_count=seen_count+excluded.seen_count""", (*key, count))
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return len(pending)
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def profile_deviations(self, events: list[LogEvent], profiles: dict[str, object]) -> dict[str, list[dict[str, object]]]:
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current: dict[tuple[str, str, str], list[float]] = defaultdict(lambda: [0, 0.0])
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for event in events:
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profile = profiles.get(event.fields.get("fgai_stream_id", ""))
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if not profile:
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continue
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entity = event.fields.get(str(getattr(profile, "entity_field", "")).lower())
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if not entity:
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continue
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numeric = {str(field).lower() for field in getattr(profile, "numeric_fields", ())}
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for field in [*getattr(profile, "categorical_fields", ()), *getattr(profile, "numeric_fields", ())]:
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key = (event.fields.get("fgai_stream_id", ""), entity, str(field).lower())
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current[key][0] += 1
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if key[2] in numeric:
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current[key][1] += _number(event.fields.get(key[2]))
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output: dict[str, list[dict[str, object]]] = defaultdict(list)
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with self._connect() as connection:
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for (stream, entity, field), values in current.items():
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rows = connection.execute("select events, numeric_sum from profile_buckets where stream_id=? and entity=? and field=? order by bucket_start desc limit 25", (stream, entity, field)).fetchall()
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if len(rows) < 12:
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continue
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if values[1] == 0:
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history = [row[0] for row in rows]
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current_value = values[0]
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reason = f"{field} event rate deviates from its stream baseline"
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else:
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history = [row[1] / row[0] if row[0] else 0 for row in rows]
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current_value = values[1] / values[0] if values[0] else 0
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reason = f"{field} value deviates from its stream baseline"
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deviation = abs(current_value - mean(history))
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if deviation > (pstdev(history) or 1.0) * 3:
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output[entity].append({"field": field, "stream_id": stream, "score": 15, "reason": reason})
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# Detect selected categorical values that have not appeared for this entity in prior data.
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for event in events:
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profile = profiles.get(event.fields.get("fgai_stream_id", ""))
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if not profile:
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continue
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entity = event.fields.get(str(getattr(profile, "entity_field", "")).lower())
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if not entity:
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continue
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stream = event.fields.get("fgai_stream_id", "")
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for field in getattr(profile, "categorical_fields", ()):
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field = str(field).lower()
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value = event.fields.get(field)
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if not value:
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continue
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known = connection.execute("select seen_count from profile_values where stream_id=? and entity=? and field=? and value=?", (stream, entity, field, value)).fetchone()
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known_total = connection.execute("select count(*) from profile_values where stream_id=? and entity=? and field=?", (stream, entity, field)).fetchone()[0]
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if known is None and known_total >= 10:
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evidence = {"field": field, "stream_id": stream, "score": 12, "reason": f"new {field} value for this entity", "value": value}
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if evidence not in output[entity]:
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output[entity].append(evidence)
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return output
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def profiles(self, source_ips: set[str]) -> dict[str, dict[str, object]]:
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profiles: dict[str, dict[str, object]] = {}
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with self._connect() as connection:
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for source_ip in source_ips:
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rows = connection.execute(
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"select events, bytes, hitcount from source_buckets where source_ip=? order by bucket_start", (source_ip,)
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).fetchall()
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# Do not compare the current (often incomplete) bucket to itself.
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rows = rows[:-1]
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if len(rows) < 12:
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continue
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rates = [row[0] * 60 / self.bucket_seconds for row in rows]
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hit_rates = [row[2] * 60 / self.bucket_seconds for row in rows]
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known = connection.execute(
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"select kind, value from source_values where source_ip=?", (source_ip,)
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).fetchall()
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profiles[source_ip] = {
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"samples": len(rows),
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"event_rate_mean": mean(rates), "event_rate_stddev": pstdev(rates) or 1.0,
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"hitcount_rate_mean": mean(hit_rates), "hitcount_rate_stddev": pstdev(hit_rates) or 1.0,
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"known_destinations": [value for kind, value in known if kind == "destination"],
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"known_destination_ports": [value for kind, value in known if kind == "destination_port"],
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}
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return profiles
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