File size: 8,273 Bytes
c4f5819 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | """Async database layer (SQLAlchemy Core).
Portable across SQLite (default, zero-config self-host) and PostgreSQL
(docker-compose / production) using the same code. Stores classified log
events; alerts and stats are computed on read.
"""
from __future__ import annotations
from collections import Counter, defaultdict
from datetime import datetime, timezone
from typing import Any
from sqlalchemy import (
JSON, BigInteger, Boolean, Column, Integer, MetaData, String, Table, Text,
func, select,
)
from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
from .classifier import CATEGORY_META
from .config import get_settings
metadata = MetaData()
# BigInteger on Postgres, but Integer on SQLite so it aliases rowid and
# autoincrements (SQLite only autoincrements INTEGER PRIMARY KEY).
PK_TYPE = BigInteger().with_variant(Integer, "sqlite")
log_events = Table(
"log_events", metadata,
Column("id", PK_TYPE, primary_key=True, autoincrement=True),
Column("ts", String(40), index=True), # event time (ISO8601)
Column("source", String(64), index=True),
Column("category", String(32), index=True), # detected category
Column("severity", String(16), index=True),
Column("mitre", String(32)),
Column("risk_score", Integer),
Column("src_ip", String(64), index=True),
Column("dst_ip", String(64)),
Column("username", String(64)),
Column("message", Text),
Column("reasons", Text),
Column("expected_category", String(32)), # original label if CSV provided one
Column("matches_label", Boolean),
Column("raw", JSON),
Column("batch_id", String(64), index=True),
Column("ingested_at", String(40), index=True),
)
_engine: AsyncEngine | None = None
def get_engine() -> AsyncEngine:
global _engine
if _engine is None:
settings = get_settings()
_engine = create_async_engine(settings.database_url, future=True, pool_pre_ping=True)
return _engine
async def init_db() -> None:
engine = get_engine()
async with engine.begin() as conn:
await conn.run_sync(metadata.create_all)
async def insert_events(events: list[dict[str, Any]]) -> int:
"""Insert enriched event rows. Returns count inserted."""
if not events:
return 0
rows = [_to_row(e) for e in events]
engine = get_engine()
async with engine.begin() as conn:
await conn.execute(log_events.insert(), rows)
return len(rows)
def _to_row(event: dict[str, Any]) -> dict[str, Any]:
return {
"ts": str(event.get("timestamp") or event.get("ts") or ""),
"source": event.get("source"),
"category": event.get("category"),
"severity": event.get("severity"),
"mitre": event.get("mitre"),
"risk_score": event.get("risk_score"),
"src_ip": event.get("src_ip"),
"dst_ip": event.get("dst_ip"),
"username": event.get("user") or event.get("username"),
"message": event.get("message"),
"reasons": "; ".join(event.get("reasons", [])) if isinstance(event.get("reasons"), list) else event.get("reasons"),
"expected_category": event.get("expected_category"),
"matches_label": event.get("matches_label"),
"raw": event.get("raw") or event,
"batch_id": event.get("batch_id"),
"ingested_at": event.get("ingested_at") or datetime.now(timezone.utc).isoformat(timespec="seconds"),
}
def _row_to_dict(row: Any) -> dict[str, Any]:
d = dict(row._mapping)
if isinstance(d.get("reasons"), str) and d["reasons"]:
d["reasons"] = d["reasons"].split("; ")
return d
async def fetch_events(limit: int = 100, offset: int = 0, category: str = "",
severity: str = "", source: str = "", q: str = "") -> dict[str, Any]:
engine = get_engine()
stmt = select(log_events)
if category:
stmt = stmt.where(log_events.c.category == category)
if severity:
stmt = stmt.where(log_events.c.severity == severity)
if source:
stmt = stmt.where(log_events.c.source == source)
if q:
stmt = stmt.where(log_events.c.message.ilike(f"%{q}%"))
count_stmt = select(func.count()).select_from(stmt.subquery())
page_stmt = stmt.order_by(log_events.c.id.desc()).limit(min(limit, 500)).offset(max(offset, 0))
async with engine.connect() as conn:
total = (await conn.execute(count_stmt)).scalar() or 0
rows = (await conn.execute(page_stmt)).fetchall()
return {"total": total, "limit": limit, "offset": offset,
"rows": [_row_to_dict(r) for r in rows]}
async def fetch_stats() -> dict[str, Any]:
engine = get_engine()
async with engine.connect() as conn:
total = (await conn.execute(select(func.count()).select_from(log_events))).scalar() or 0
cat_rows = (await conn.execute(
select(log_events.c.category, func.count()).group_by(log_events.c.category))).fetchall()
sev_rows = (await conn.execute(
select(log_events.c.severity, func.count()).group_by(log_events.c.severity))).fetchall()
src_rows = (await conn.execute(
select(log_events.c.source, func.count()).group_by(log_events.c.source))).fetchall()
ip_rows = (await conn.execute(
select(log_events.c.src_ip, func.count()).where(log_events.c.src_ip.isnot(None))
.group_by(log_events.c.src_ip).order_by(func.count().desc()).limit(10))).fetchall()
by_category = {k or "unknown": v for k, v in cat_rows}
risky = sum(v for k, v in by_category.items() if k != "benign")
return {
"total": total,
"risky": risky,
"risk_rate": round((risky / total) * 100, 2) if total else 0.0,
"by_category": by_category,
"by_severity": {k or "unknown": v for k, v in sev_rows},
"by_source": {k or "unknown": v for k, v in src_rows},
"top_ips": [[k, v] for k, v in ip_rows],
"categories": CATEGORY_META,
}
async def fetch_alerts(limit: int = 20) -> dict[str, Any]:
"""Group critical events into alerts (computed on read)."""
engine = get_engine()
stmt = select(log_events).where(log_events.c.severity == "critical").order_by(log_events.c.id.desc()).limit(2000)
async with engine.connect() as conn:
rows = [_row_to_dict(r) for r in (await conn.execute(stmt)).fetchall()]
groups: dict[str, list[dict[str, Any]]] = defaultdict(list)
for row in rows:
if row.get("category") == "priv_esc":
key = "|".join([str(row.get("category")), str(row.get("username")), str(row.get("src_ip"))])
else:
key = "|".join([str(row.get("category")), str(row.get("src_ip")), str(row.get("dst_ip")), str(row.get("username"))])
groups[key].append(row)
alerts = []
for key, items in groups.items():
items.sort(key=lambda r: str(r.get("ts", "")))
first, last = items[0], items[-1]
meta = CATEGORY_META.get(first.get("category"), CATEGORY_META["benign"])
alerts.append({
"title": meta["label"],
"category": first.get("category"),
"severity": "critical",
"mitre": meta["mitre"],
"count": len(items),
"first_seen": first.get("ts"),
"last_seen": last.get("ts"),
"src_ip": first.get("src_ip") or "-",
"dst_ip": first.get("dst_ip") or "-",
"user": first.get("username") or "-",
"message": last.get("message"),
"reasons": last.get("reasons"),
"recommended_action": meta["runbook"],
})
alerts.sort(key=lambda a: str(a["last_seen"]), reverse=True)
return {"total": len(alerts), "limit": limit, "alerts": alerts[:limit]}
async def search_rows(q: str, limit: int = 400) -> list[dict[str, Any]]:
"""Fetch candidate rows for keyword RAG (message LIKE any token)."""
engine = get_engine()
stmt = select(log_events).order_by(log_events.c.id.desc()).limit(limit)
if q:
stmt = select(log_events).where(log_events.c.message.ilike(f"%{q}%")).order_by(log_events.c.id.desc()).limit(limit)
async with engine.connect() as conn:
rows = (await conn.execute(stmt)).fetchall()
return [_row_to_dict(r) for r in rows]
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