Feat/attack map improvement (#57)
* feat: enhance IP reputation management with city data and geolocation integration * feat: enhance dashboard with city coordinates and improved marker handling * feat: update chart version to 0.2.1 in Chart.yaml, README.md, and values.yaml * feat: update logo format and size in README.md * feat: improve location display logic in dashboard for attackers and IPs
This commit is contained in:
Lorenzo Venerandi
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GitHub
GitHub
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8c76f6c847
commit
5aca684df9
@@ -2,8 +2,21 @@
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"""
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Test script to insert fake external IPs into the database for testing the dashboard.
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This generates realistic-looking test data including access logs, credential attempts, and attack detections.
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Also triggers category behavior changes to demonstrate the timeline feature.
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This generates realistic-looking test data including:
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- Access logs with various suspicious activities
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- Credential attempts
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- Attack detections (SQL injection, XSS, etc.)
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- Category behavior changes for timeline demonstration
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- Real good crawler IPs (Googlebot, Bingbot, etc.) with API-fetched geolocation
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Usage:
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python test_insert_fake_ips.py [num_ips] [logs_per_ip] [credentials_per_ip] [--no-cleanup]
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Examples:
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python test_insert_fake_ips.py # Generate 20 IPs with defaults, cleanup DB first
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python test_insert_fake_ips.py 30 # Generate 30 IPs with defaults
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python test_insert_fake_ips.py 30 20 5 # Generate 30 IPs, 20 logs each, 5 credentials each
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python test_insert_fake_ips.py --no-cleanup # Generate data without cleaning DB first
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"""
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import random
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@@ -12,6 +25,7 @@ import sys
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from datetime import datetime, timedelta
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from zoneinfo import ZoneInfo
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from pathlib import Path
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import requests
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# Add parent src directory to path so we can import database and logger
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sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
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@@ -23,14 +37,81 @@ from logger import get_app_logger
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# TEST DATA GENERATORS
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# ----------------------
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FAKE_IPS = [
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"203.0.113.45", # Regular attacker IP
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"198.51.100.89", # Credential harvester IP
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"192.0.2.120", # Bot IP
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"205.32.180.65", # Another attacker
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"210.45.67.89", # Suspicious IP
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"175.23.45.67", # International IP
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"182.91.102.45", # Another suspicious IP
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# Fake IPs with geolocation data (country_code, city, ASN org)
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# These will appear on the map based on their country_code
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FAKE_IPS_WITH_GEO = [
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# United States
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("45.142.120.10", "US", "New York", "AS14061 DigitalOcean"),
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("107.189.10.143", "US", "Los Angeles", "AS20473 Vultr"),
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("162.243.175.23", "US", "San Francisco", "AS14061 DigitalOcean"),
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("198.51.100.89", "US", "Chicago", "AS16509 Amazon"),
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# Europe
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("185.220.101.45", "DE", "Berlin", "AS24940 Hetzner"),
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("195.154.133.20", "FR", "Paris", "AS12876 Scaleway"),
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("178.128.83.165", "GB", "London", "AS14061 DigitalOcean"),
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("87.251.67.90", "NL", "Amsterdam", "AS49453 GlobalConnect"),
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("91.203.5.165", "RU", "Moscow", "AS51115 HLL LLC"),
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("46.105.57.169", "FR", "Roubaix", "AS16276 OVH"),
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("217.182.143.207", "RU", "Saint Petersburg", "AS51570 JSC ER-Telecom"),
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("188.166.123.45", "GB", "Manchester", "AS14061 DigitalOcean"),
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# Asia
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("103.253.145.36", "CN", "Beijing", "AS4134 Chinanet"),
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("42.112.28.216", "CN", "Shanghai", "AS4134 Chinanet"),
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("118.163.74.160", "JP", "Tokyo", "AS2516 KDDI"),
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("43.229.53.35", "SG", "Singapore", "AS23969 TOT"),
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("115.78.208.140", "IN", "Mumbai", "AS9829 BSNL"),
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("14.139.56.18", "IN", "Bangalore", "AS4755 TATA"),
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("61.19.25.207", "TW", "Taipei", "AS3462 HiNet"),
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("121.126.219.198", "KR", "Seoul", "AS4766 Korea Telecom"),
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("202.134.4.212", "ID", "Jakarta", "AS7597 TELKOMNET"),
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("171.244.140.134", "VN", "Hanoi", "AS7552 Viettel"),
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# South America
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("177.87.169.20", "BR", "São Paulo", "AS28573 Claro"),
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("200.21.19.58", "BR", "Rio de Janeiro", "AS7738 Telemar"),
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("181.13.140.98", "AR", "Buenos Aires", "AS7303 Telecom Argentina"),
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("190.150.24.34", "CO", "Bogotá", "AS3816 Colombia Telecomunicaciones"),
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# Middle East & Africa
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("41.223.53.141", "EG", "Cairo", "AS8452 TE-Data"),
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("196.207.35.152", "ZA", "Johannesburg", "AS37271 Workonline"),
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("5.188.62.214", "TR", "Istanbul", "AS51115 HLL LLC"),
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("37.48.93.125", "AE", "Dubai", "AS5384 Emirates Telecom"),
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("102.66.137.29", "NG", "Lagos", "AS29465 MTN Nigeria"),
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# Australia & Oceania
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("103.28.248.110", "AU", "Sydney", "AS4739 Internode"),
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("202.168.45.33", "AU", "Melbourne", "AS1221 Telstra"),
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# Additional European IPs
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("94.102.49.190", "PL", "Warsaw", "AS12912 T-Mobile"),
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("213.32.93.140", "ES", "Madrid", "AS3352 Telefónica"),
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("79.137.79.167", "IT", "Rome", "AS3269 Telecom Italia"),
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("37.9.169.146", "SE", "Stockholm", "AS3301 Telia"),
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("188.92.80.123", "RO", "Bucharest", "AS8708 RCS & RDS"),
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("80.240.25.198", "CZ", "Prague", "AS6830 UPC"),
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]
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# Extract just IPs for backward compatibility
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FAKE_IPS = [ip_data[0] for ip_data in FAKE_IPS_WITH_GEO]
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# Create geo data dictionary
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FAKE_GEO_DATA = {
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ip_data[0]: (ip_data[1], ip_data[2], ip_data[3])
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for ip_data in FAKE_IPS_WITH_GEO
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}
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# Real good crawler IPs (Googlebot, Bingbot, etc.) - geolocation will be fetched from API
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GOOD_CRAWLER_IPS = [
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"66.249.66.1", # Googlebot
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"66.249.79.23", # Googlebot
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"40.77.167.52", # Bingbot
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"157.55.39.145", # Bingbot
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"17.58.98.100", # Applebot
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"199.59.150.39", # Twitterbot
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"54.236.1.15", # Amazon Bot
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]
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FAKE_PATHS = [
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@@ -79,11 +160,11 @@ ATTACK_TYPES = [
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]
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CATEGORIES = [
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"ATTACKER",
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"BAD_CRAWLER",
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"GOOD_CRAWLER",
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"REGULAR_USER",
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"UNKNOWN",
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"attacker",
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"bad_crawler",
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"good_crawler",
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"regular_user",
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"unknown",
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]
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@@ -109,14 +190,92 @@ def generate_analyzed_metrics():
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}
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def generate_fake_data(num_ips: int = 45, logs_per_ip: int = 15, credentials_per_ip: int = 3):
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def cleanup_database(db_manager, app_logger):
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"""
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Clean up all existing test data from the database.
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Args:
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db_manager: Database manager instance
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app_logger: Logger instance
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"""
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from models import AccessLog, CredentialAttempt, AttackDetection, IpStats, CategoryHistory
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app_logger.info("=" * 60)
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app_logger.info("Cleaning up existing database data")
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app_logger.info("=" * 60)
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session = db_manager.session
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try:
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# Delete all records from each table
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deleted_attack_detections = session.query(AttackDetection).delete()
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deleted_access_logs = session.query(AccessLog).delete()
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deleted_credentials = session.query(CredentialAttempt).delete()
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deleted_category_history = session.query(CategoryHistory).delete()
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deleted_ip_stats = session.query(IpStats).delete()
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session.commit()
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app_logger.info(f"Deleted {deleted_access_logs} access logs")
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app_logger.info(f"Deleted {deleted_attack_detections} attack detections")
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app_logger.info(f"Deleted {deleted_credentials} credential attempts")
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app_logger.info(f"Deleted {deleted_category_history} category history records")
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app_logger.info(f"Deleted {deleted_ip_stats} IP statistics")
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app_logger.info("✓ Database cleanup complete")
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except Exception as e:
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session.rollback()
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app_logger.error(f"Error during database cleanup: {e}")
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raise
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finally:
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db_manager.close_session()
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def fetch_geolocation_from_api(ip: str, app_logger) -> tuple:
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"""
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Fetch geolocation data from the IP reputation API.
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Args:
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ip: IP address to lookup
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app_logger: Logger instance
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Returns:
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Tuple of (country_code, city, asn, asn_org) or None if failed
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"""
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try:
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api_url = "https://iprep.lcrawl.com/api/iprep/"
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params = {"cidr": ip}
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headers = {"Content-Type": "application/json"}
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response = requests.get(api_url, headers=headers, params=params, timeout=10)
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if response.status_code == 200:
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payload = response.json()
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if payload.get("results"):
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data = payload["results"][0]
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geoip_data = data.get("geoip_data", {})
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country_code = geoip_data.get("country_iso_code", "Unknown")
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city = geoip_data.get("city_name", "Unknown")
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asn = geoip_data.get("asn_autonomous_system_number")
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asn_org = geoip_data.get("asn_autonomous_system_organization", "Unknown")
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return (country_code, city, asn, asn_org)
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except requests.RequestException as e:
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app_logger.warning(f"Failed to fetch geolocation for {ip}: {e}")
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except Exception as e:
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app_logger.error(f"Error processing geolocation for {ip}: {e}")
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return None
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def generate_fake_data(num_ips: int = 20, logs_per_ip: int = 15, credentials_per_ip: int = 3, include_good_crawlers: bool = True, cleanup: bool = True):
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"""
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Generate and insert fake test data into the database.
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Args:
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num_ips: Number of unique fake IPs to generate (default: 5)
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num_ips: Number of unique fake IPs to generate (default: 20)
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logs_per_ip: Number of access logs per IP (default: 15)
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credentials_per_ip: Number of credential attempts per IP (default: 3)
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include_good_crawlers: Whether to add real good crawler IPs with API-fetched geolocation (default: True)
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cleanup: Whether to clean up existing database data before generating new data (default: True)
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"""
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db_manager = get_database()
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app_logger = get_app_logger()
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@@ -125,6 +284,11 @@ def generate_fake_data(num_ips: int = 45, logs_per_ip: int = 15, credentials_per
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if not db_manager._initialized:
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db_manager.initialize()
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# Clean up existing data if requested
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if cleanup:
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cleanup_database(db_manager, app_logger)
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print() # Add blank line for readability
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app_logger.info("=" * 60)
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app_logger.info("Starting fake IP data generation for testing")
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app_logger.info("=" * 60)
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@@ -186,6 +350,28 @@ def generate_fake_data(num_ips: int = 45, logs_per_ip: int = 15, credentials_per
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app_logger.info(f" ✓ Generated {logs_per_ip} access logs")
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app_logger.info(f" ✓ Generated {credentials_per_ip} credential attempts")
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# Add geolocation data if available for this IP
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if ip in FAKE_GEO_DATA:
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country_code, city, asn_org = FAKE_GEO_DATA[ip]
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# Extract ASN number from ASN string (e.g., "AS12345 Name" -> 12345)
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asn_number = None
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if asn_org and asn_org.startswith("AS"):
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try:
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asn_number = int(asn_org.split()[0][2:]) # Remove "AS" prefix and get number
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except (ValueError, IndexError):
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asn_number = 12345 # Fallback
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# Update IP reputation info including geolocation and city
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db_manager.update_ip_rep_infos(
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ip=ip,
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country_code=country_code,
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asn=asn_number or 12345,
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asn_org=asn_org,
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list_on={},
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city=city # Now passing city to the function
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)
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app_logger.info(f" 📍 Added geolocation: {city}, {country_code} ({asn_org})")
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# Trigger behavior/category changes to demonstrate timeline feature
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# First analysis
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initial_category = random.choice(CATEGORIES)
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@@ -232,11 +418,79 @@ def generate_fake_data(num_ips: int = 45, logs_per_ip: int = 15, credentials_per
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)
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total_category_changes += 1
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# Add good crawler IPs with real geolocation from API
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total_good_crawlers = 0
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if include_good_crawlers:
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app_logger.info("\n" + "=" * 60)
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app_logger.info("Adding Good Crawler IPs with API-fetched geolocation")
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app_logger.info("=" * 60)
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for crawler_ip in GOOD_CRAWLER_IPS:
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app_logger.info(f"\nProcessing Good Crawler: {crawler_ip}")
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# Fetch real geolocation from API
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geo_data = fetch_geolocation_from_api(crawler_ip, app_logger)
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# Don't generate access logs for good crawlers to prevent re-categorization
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# We'll just create the IP stats entry with the category set
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app_logger.info(f" ✓ Adding as good crawler (no logs to prevent re-categorization)")
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# First, we need to create the IP in the database via persist_access
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# (but we'll only create one minimal log entry)
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db_manager.persist_access(
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ip=crawler_ip,
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path="/robots.txt", # Minimal, normal crawler behavior
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user_agent="Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)",
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method="GET",
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is_suspicious=False,
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is_honeypot_trigger=False,
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attack_types=None,
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)
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# Add geolocation if API fetch was successful
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if geo_data:
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country_code, city, asn, asn_org = geo_data
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db_manager.update_ip_rep_infos(
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ip=crawler_ip,
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country_code=country_code,
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asn=asn if asn else 12345,
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asn_org=asn_org,
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list_on={},
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city=city
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)
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app_logger.info(f" 📍 API-fetched geolocation: {city}, {country_code} ({asn_org})")
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else:
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app_logger.warning(f" ⚠ Could not fetch geolocation for {crawler_ip}")
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# Set category to good_crawler - this sets manual_category=True to prevent re-analysis
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db_manager.update_ip_stats_analysis(
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ip=crawler_ip,
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analyzed_metrics={
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"request_frequency": 0.1, # Very low frequency
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"suspicious_patterns": 0,
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"credential_attempts": 0,
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"attack_diversity": 0.0,
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},
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category="good_crawler",
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category_scores={
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"attacker": 0,
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"good_crawler": 100,
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"bad_crawler": 0,
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"regular_user": 0,
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"unknown": 0,
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},
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last_analysis=datetime.now(tz=ZoneInfo('UTC'))
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)
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total_good_crawlers += 1
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time.sleep(0.5) # Small delay between API calls
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# Print summary
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app_logger.info("\n" + "=" * 60)
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app_logger.info("Test Data Generation Complete!")
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app_logger.info("=" * 60)
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app_logger.info(f"Total IPs created: {len(selected_ips)}")
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app_logger.info(f"Total IPs created: {len(selected_ips) + total_good_crawlers}")
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app_logger.info(f" - Attackers/Mixed: {len(selected_ips)}")
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app_logger.info(f" - Good Crawlers: {total_good_crawlers}")
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app_logger.info(f"Total access logs: {total_logs}")
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app_logger.info(f"Total attack detections: {total_attacks}")
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app_logger.info(f"Total credential attempts: {total_credentials}")
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@@ -244,6 +498,7 @@ def generate_fake_data(num_ips: int = 45, logs_per_ip: int = 15, credentials_per
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app_logger.info("=" * 60)
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app_logger.info("\nYou can now view the dashboard with this test data.")
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app_logger.info("The 'Behavior Timeline' will show category transitions for each IP.")
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app_logger.info("The map will show good crawlers with real geolocation from API.")
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app_logger.info("Run: python server.py")
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app_logger.info("=" * 60)
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@@ -252,8 +507,10 @@ if __name__ == "__main__":
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import sys
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# Allow command-line arguments for customization
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num_ips = int(sys.argv[1]) if len(sys.argv) > 1 else 5
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num_ips = int(sys.argv[1]) if len(sys.argv) > 1 else 20
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logs_per_ip = int(sys.argv[2]) if len(sys.argv) > 2 else 15
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credentials_per_ip = int(sys.argv[3]) if len(sys.argv) > 3 else 3
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# Add --no-cleanup flag to skip database cleanup
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cleanup = "--no-cleanup" not in sys.argv
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generate_fake_data(num_ips, logs_per_ip, credentials_per_ip)
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generate_fake_data(num_ips, logs_per_ip, credentials_per_ip, include_good_crawlers=True, cleanup=cleanup)
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