284 lines
14 KiB
Python
284 lines
14 KiB
Python
#!/usr/bin/env python3
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from sqlalchemy import select
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from typing import Optional
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from database import get_database, DatabaseManager
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from zoneinfo import ZoneInfo
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from pathlib import Path
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from datetime import datetime, timedelta
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import re
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from wordlists import get_wordlists
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from config import get_config
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"""
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Functions for user activity analysis
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"""
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class Analyzer:
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"""
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Analyzes users activity and produces aggregated insights
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"""
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def __init__(self, db_manager: Optional[DatabaseManager] = None, timezone: Optional[ZoneInfo] = None):
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"""
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Initialize the access tracker.
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Args:
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db_manager: Optional DatabaseManager for persistence.
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If None, will use the global singleton.
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"""
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self.timezone = timezone or ZoneInfo('UTC')
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# Database manager for persistence (lazily initialized)
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self._db_manager = db_manager
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@property
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def db(self) -> Optional[DatabaseManager]:
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"""
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Get the database manager, lazily initializing if needed.
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Returns:
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DatabaseManager instance or None if not available
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"""
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if self._db_manager is None:
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try:
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self._db_manager = get_database()
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except Exception:
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# Database not initialized, persistence disabled
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pass
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return self._db_manager
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def infer_user_category(self, ip: str) -> str:
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config = get_config()
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http_risky_methods_threshold = config.http_risky_methods_threshold
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violated_robots_threshold = config.violated_robots_threshold
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uneven_request_timing_threshold = config.uneven_request_timing_threshold
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user_agents_used_threshold = config.user_agents_used_threshold
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attack_urls_threshold = config.attack_urls_threshold
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uneven_request_timing_time_window_seconds = config.uneven_request_timing_time_window_seconds
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print(f"http_risky_methods_threshold: {http_risky_methods_threshold}")
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score = {}
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score["attacker"] = {"risky_http_methods": False, "robots_violations": False, "uneven_request_timing": False, "different_user_agents": False, "attack_url": False}
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score["good_crawler"] = {"risky_http_methods": False, "robots_violations": False, "uneven_request_timing": False, "different_user_agents": False, "attack_url": False}
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score["bad_crawler"] = {"risky_http_methods": False, "robots_violations": False, "uneven_request_timing": False, "different_user_agents": False, "attack_url": False}
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score["regular_user"] = {"risky_http_methods": False, "robots_violations": False, "uneven_request_timing": False, "different_user_agents": False, "attack_url": False}
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#1-3 low, 4-6 mid, 7-9 high, 10-20 extreme
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weights = {
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"attacker": {
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"risky_http_methods": 6,
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"robots_violations": 4,
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"uneven_request_timing": 3,
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"different_user_agents": 8,
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"attack_url": 15
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},
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"good_crawler": {
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"risky_http_methods": 1,
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"robots_violations": 0,
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"uneven_request_timing": 0,
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"different_user_agents": 0,
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"attack_url": 0
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},
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"bad_crawler": {
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"risky_http_methods": 2,
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"robots_violations": 7,
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"uneven_request_timing": 0,
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"different_user_agents": 5,
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"attack_url": 5
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},
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"regular_user": {
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"risky_http_methods": 0,
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"robots_violations": 0,
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"uneven_request_timing": 8,
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"different_user_agents": 3,
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"attack_url": 0
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}
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}
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accesses = self.db.get_access_logs(ip_filter = ip, limit=1000)
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total_accesses_count = len(accesses)
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if total_accesses_count <= 0:
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return
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#--------------------- HTTP Methods ---------------------
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get_accesses_count = len([item for item in accesses if item["method"] == "GET"])
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post_accesses_count = len([item for item in accesses if item["method"] == "POST"])
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put_accesses_count = len([item for item in accesses if item["method"] == "PUT"])
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delete_accesses_count = len([item for item in accesses if item["method"] == "DELETE"])
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head_accesses_count = len([item for item in accesses if item["method"] == "HEAD"])
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options_accesses_count = len([item for item in accesses if item["method"] == "OPTIONS"])
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patch_accesses_count = len([item for item in accesses if item["method"] == "PATCH"])
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if total_accesses_count > http_risky_methods_threshold:
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http_method_attacker_score = (post_accesses_count + put_accesses_count + delete_accesses_count + options_accesses_count + patch_accesses_count) / total_accesses_count
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else:
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http_method_attacker_score = 0
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#print(f"HTTP Method attacker score: {http_method_attacker_score}")
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if http_method_attacker_score >= http_risky_methods_threshold:
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score["attacker"]["risky_http_methods"] = True
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score["good_crawler"]["risky_http_methods"] = False
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score["bad_crawler"]["risky_http_methods"] = True
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score["regular_user"]["risky_http_methods"] = False
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else:
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score["attacker"]["risky_http_methods"] = False
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score["good_crawler"]["risky_http_methods"] = True
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score["bad_crawler"]["risky_http_methods"] = False
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score["regular_user"]["risky_http_methods"] = False
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#--------------------- Robots Violations ---------------------
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#respect robots.txt and login/config pages access frequency
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robots_disallows = []
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robots_path = Path(__file__).parent / "templates" / "html" / "robots.txt"
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with open(robots_path, "r") as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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parts = line.split(":")
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if parts[0] == "Disallow":
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parts[1] = parts[1].rstrip("/")
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#print(f"DISALLOW {parts[1]}")
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robots_disallows.append(parts[1].strip())
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#if 0 100% sure is good crawler, if >10% of robots violated is bad crawler or attacker
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violated_robots_count = len([item for item in accesses if item["path"].rstrip("/") in tuple(robots_disallows)])
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#print(f"Violated robots count: {violated_robots_count}")
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if total_accesses_count > 0:
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violated_robots_ratio = violated_robots_count / total_accesses_count
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else:
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violated_robots_ratio = 0
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if violated_robots_ratio >= violated_robots_threshold:
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score["attacker"]["robots_violations"] = True
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score["good_crawler"]["robots_violations"] = False
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score["bad_crawler"]["robots_violations"] = True
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score["regular_user"]["robots_violations"] = False
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else:
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score["attacker"]["robots_violations"] = False
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score["good_crawler"]["robots_violations"] = False
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score["bad_crawler"]["robots_violations"] = False
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score["regular_user"]["robots_violations"] = False
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#--------------------- Requests Timing ---------------------
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#Request rate and timing: steady, throttled, polite vs attackers' bursty, aggressive, or oddly rhythmic behavior
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timestamps = [datetime.fromisoformat(item["timestamp"]) for item in accesses]
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timestamps = [ts for ts in timestamps if datetime.utcnow() - ts <= timedelta(seconds=uneven_request_timing_time_window_seconds)]
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timestamps = sorted(timestamps, reverse=True)
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time_diffs = []
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for i in range(0, len(timestamps)-1):
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diff = (timestamps[i] - timestamps[i+1]).total_seconds()
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time_diffs.append(diff)
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mean = 0
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variance = 0
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std = 0
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cv = 0
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if time_diffs:
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mean = sum(time_diffs) / len(time_diffs)
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variance = sum((x - mean) ** 2 for x in time_diffs) / len(time_diffs)
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std = variance ** 0.5
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cv = std/mean
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print(f"Mean: {mean} - Variance {variance} - Standard Deviation {std} - Coefficient of Variation: {cv}")
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if cv >= uneven_request_timing_threshold:
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score["attacker"]["uneven_request_timing"] = True
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score["good_crawler"]["uneven_request_timing"] = False
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score["bad_crawler"]["uneven_request_timing"] = False
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score["regular_user"]["uneven_request_timing"] = True
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else:
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score["attacker"]["uneven_request_timing"] = False
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score["good_crawler"]["uneven_request_timing"] = False
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score["bad_crawler"]["uneven_request_timing"] = False
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score["regular_user"]["uneven_request_timing"] = False
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#--------------------- Different User Agents ---------------------
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#Header Quality and Consistency: Crawlers tend to use complete and consistent headers, attackers might miss, fake, or change headers
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user_agents_used = [item["user_agent"] for item in accesses]
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user_agents_used = list(dict.fromkeys(user_agents_used))
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#print(f"User agents used: {user_agents_used}")
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if len(user_agents_used) >= user_agents_used_threshold:
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score["attacker"]["different_user_agents"] = True
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score["good_crawler"]["different_user_agents"] = False
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score["bad_crawler"]["different_user_agentss"] = True
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score["regular_user"]["different_user_agents"] = False
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else:
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score["attacker"]["different_user_agents"] = False
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score["good_crawler"]["different_user_agents"] = False
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score["bad_crawler"]["different_user_agents"] = False
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score["regular_user"]["different_user_agents"] = False
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#--------------------- Attack URLs ---------------------
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attack_urls_found_list = []
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wl = get_wordlists()
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if wl.attack_urls:
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queried_paths = [item["path"] for item in accesses]
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for queried_path in queried_paths:
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for name, pattern in wl.attack_urls.items():
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if re.search(pattern, queried_path, re.IGNORECASE):
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attack_urls_found_list.append(pattern)
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if len(attack_urls_found_list) > attack_urls_threshold:
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score["attacker"]["attack_url"] = True
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score["good_crawler"]["attack_url"] = False
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score["bad_crawler"]["attack_url"] = False
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score["regular_user"]["attack_url"] = False
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else:
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score["attacker"]["attack_url"] = False
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score["good_crawler"]["attack_url"] = False
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score["bad_crawler"]["attack_url"] = False
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score["regular_user"]["attack_url"] = False
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#--------------------- Calculate score ---------------------
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attacker_score = good_crawler_score = bad_crawler_score = regular_user_score = 0
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attacker_score = score["attacker"]["risky_http_methods"] * weights["attacker"]["risky_http_methods"]
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attacker_score = attacker_score + score["attacker"]["robots_violations"] * weights["attacker"]["robots_violations"]
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attacker_score = attacker_score + score["attacker"]["uneven_request_timing"] * weights["attacker"]["uneven_request_timing"]
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attacker_score = attacker_score + score["attacker"]["different_user_agents"] * weights["attacker"]["different_user_agents"]
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attacker_score = attacker_score + score["attacker"]["attack_url"] * weights["attacker"]["attack_url"]
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good_crawler_score = score["good_crawler"]["risky_http_methods"] * weights["good_crawler"]["risky_http_methods"]
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good_crawler_score = good_crawler_score + score["good_crawler"]["robots_violations"] * weights["good_crawler"]["robots_violations"]
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good_crawler_score = good_crawler_score + score["good_crawler"]["uneven_request_timing"] * weights["good_crawler"]["uneven_request_timing"]
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good_crawler_score = good_crawler_score + score["good_crawler"]["different_user_agents"] * weights["good_crawler"]["different_user_agents"]
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good_crawler_score = good_crawler_score + score["good_crawler"]["attack_url"] * weights["good_crawler"]["attack_url"]
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bad_crawler_score = score["bad_crawler"]["risky_http_methods"] * weights["bad_crawler"]["risky_http_methods"]
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bad_crawler_score = bad_crawler_score + score["bad_crawler"]["robots_violations"] * weights["bad_crawler"]["robots_violations"]
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bad_crawler_score = bad_crawler_score + score["bad_crawler"]["uneven_request_timing"] * weights["bad_crawler"]["uneven_request_timing"]
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bad_crawler_score = bad_crawler_score + score["bad_crawler"]["different_user_agents"] * weights["bad_crawler"]["different_user_agents"]
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bad_crawler_score = bad_crawler_score + score["bad_crawler"]["attack_url"] * weights["bad_crawler"]["attack_url"]
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regular_user_score = score["regular_user"]["risky_http_methods"] * weights["regular_user"]["risky_http_methods"]
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regular_user_score = regular_user_score + score["regular_user"]["robots_violations"] * weights["regular_user"]["robots_violations"]
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regular_user_score = regular_user_score + score["regular_user"]["uneven_request_timing"] * weights["regular_user"]["uneven_request_timing"]
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regular_user_score = regular_user_score + score["regular_user"]["different_user_agents"] * weights["regular_user"]["different_user_agents"]
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regular_user_score = regular_user_score + score["regular_user"]["attack_url"] * weights["regular_user"]["attack_url"]
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print(f"Attacker score: {attacker_score}")
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print(f"Good Crawler score: {good_crawler_score}")
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print(f"Bad Crawler score: {bad_crawler_score}")
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print(f"Regular User score: {regular_user_score}")
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analyzed_metrics = {"risky_http_methods": http_method_attacker_score, "robots_violations": violated_robots_ratio, "uneven_request_timing": mean, "different_user_agents": user_agents_used, "attack_url": attack_urls_found_list}
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category_scores = {"attacker": attacker_score, "good_crawler": good_crawler_score, "bad_crawler": bad_crawler_score, "regular_user": regular_user_score}
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category = max(category_scores, key=category_scores.get)
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last_analysis = datetime.utcnow()
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self._db_manager.update_ip_stats_analysis(ip, analyzed_metrics, category, category_scores, last_analysis)
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return 0
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