Datasets:
Formats:
csv
Languages:
English
Size:
10M - 100M
Tags:
cybersecurity
intrusion-detection
network-security
vanet
vehicular-networks
federated-learning
License:
| from pathlib import Path | |
| import csv | |
| import gzip | |
| from collections import Counter | |
| import sys | |
| ROOT = Path("/mnt/c/VANET_Dataset/fl-bert-vanet-dataset") | |
| sys.path.insert(0, str(ROOT / "scripts")) | |
| from build_vanet_ids26_master import ( | |
| FIELDNAMES, | |
| standard_base_row, | |
| standard_overlay_row, | |
| ) | |
| OUT = ROOT / "release" / "github" / "VANET-IDS26" / "samples" / "vanet_ids26_sample.csv.gz" | |
| BENIGN_LIMIT = 1000 | |
| PER_ATTACK_LIMIT = 1000 | |
| ATTACK_TYPES = { | |
| 1: "constant_position", | |
| 2: "position_offset", | |
| 3: "random_position", | |
| 4: "speed_manipulation", | |
| 5: "acceleration_manipulation", | |
| 6: "heading_manipulation", | |
| 7: "lane_spoofing", | |
| 8: "impossible_kinematics", | |
| 9: "eventual_stop", | |
| 10: "false_brake_event", | |
| 11: "false_emergency_vehicle", | |
| 12: "false_hazard_event", | |
| 13: "replay", | |
| 14: "delayed_message", | |
| 15: "timestamp_shift", | |
| 16: "stale_message_replay", | |
| 17: "sybil", | |
| 18: "impersonation", | |
| 19: "pseudonym_abuse", | |
| 20: "flooding_ddos", | |
| 21: "beacon_rate_abuse", | |
| 22: "gnss_spoofing", | |
| 23: "map_location_spoofing", | |
| 24: "ghost_vehicle", | |
| 25: "false_object_injection", | |
| 26: "object_position_shift", | |
| } | |
| def read_csv(path): | |
| with path.open("r", newline="", errors="replace") as f: | |
| reader = csv.DictReader(f) | |
| for row in reader: | |
| yield row | |
| def main(): | |
| OUT.parent.mkdir(parents=True, exist_ok=True) | |
| counts = Counter() | |
| with gzip.open(OUT, "wt", newline="", compresslevel=1, encoding="utf-8") as gz: | |
| writer = csv.DictWriter(gz, fieldnames=FIELDNAMES) | |
| writer.writeheader() | |
| base_path = ROOT / "dataset" / "processed" / "run_001001" / "messages.csv" | |
| print(f"Sampling benign rows from {base_path.relative_to(ROOT)}") | |
| for row in read_csv(base_path): | |
| writer.writerow(standard_base_row(row, base_path)) | |
| counts[0] += 1 | |
| if counts[0] >= BENIGN_LIMIT: | |
| break | |
| for label, attack in ATTACK_TYPES.items(): | |
| path = ROOT / f"dataset/overlays/run_001001/{attack}_10/attack_overlay.csv" | |
| if not path.exists(): | |
| matches = sorted((ROOT / "dataset" / "overlays").glob(f"run_001*/{attack}_*/attack_overlay.csv")) | |
| if not matches: | |
| print(f"WARNING: no overlay found for attack {label}: {attack}") | |
| continue | |
| path = matches[0] | |
| print(f"Sampling attack {label:02d} {attack} from {path.relative_to(ROOT)}") | |
| for row in read_csv(path): | |
| writer.writerow(standard_overlay_row(row, path)) | |
| counts[label] += 1 | |
| if counts[label] >= PER_ATTACK_LIMIT: | |
| break | |
| print() | |
| print("DONE") | |
| print(f"Output: {OUT}") | |
| print("Counts by multiclass label:") | |
| for label in range(27): | |
| print(f"{label}: {counts[label]}") | |
| print(f"Total rows excluding header: {sum(counts.values())}") | |
| if __name__ == "__main__": | |
| main() | |