feat: rewrite ASN classification with PeeringDB + expanded heuristics

Major improvements to generate_asn_data.py:
- Add PeeringDB network data source (34K networks with info_type)
- Add new categories: education, government, enterprise
- Rename 'human' label to 'isp' across all consumers
- Expand keyword heuristics (ISP, datacenter, hosting, CDN, education, gov)
- Add hard-coded lists for education, government, enterprise ASNs
- Support both --output-dir and --output-asn/--output-ipasn CLI interfaces
- Add --no-peeringdb flag for offline use

Results: unknown dropped from 86% to 57%, ISP coverage 21.8K ASNs,
education 3.1K, enterprise 5.7K, government 520.

Updated consumers:
- bot_detector.py: 'human' -> 'isp' for baseline selection
- dashboard api.py: 'human' -> 'isp' in SQL queries
- run-tests.sh: 'human' -> 'isp' in integration test assertions
- update-csv-data.sh: updated label description comment

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

services/dashboard/backend/routes/api.py

@@ -469,11 +469,11 @@ async def features() -> dict[str, Any]:
     except Exception:
         logger.debug("view_thesis_features_1h not available")
 
-    # Human vs bot feature profiles for radar comparison
+    # ISP (residential) vs bot feature profiles for radar comparison
     try:
         human = query(
             f"SELECT {_feat_cols} FROM {_DB}.view_ai_features_1h "
-            "WHERE asn_label = 'human'"
+            "WHERE asn_label = 'isp'"
         )
         if human:
             result["human_profile"] = human[0]
@@ -663,7 +663,7 @@ async def heatmap() -> dict[str, Any]:
 
 
 # ---------------------------------------------------------------------------
-# GET /api/ip/{ip}/radar — Radar comparison vs human baseline
+# GET /api/ip/{ip}/radar — Radar comparison vs ISP baseline
 # ---------------------------------------------------------------------------
 _RADAR_FEATURES = [
     "hit_velocity", "fuzzing_index", "post_ratio", "asset_ratio",
@@ -686,7 +686,7 @@ async def ip_radar(ip: str) -> dict[str, Any]:
         baseline = query(
             f"SELECT {cols_avg} "
             f"FROM {_DB}.view_ai_features_1h "
-            "WHERE asn_label = 'human'"
+            "WHERE asn_label = 'isp'"
         )
         bot_avg = query(
             f"SELECT {cols_avg} "