Security Analytics and Threat Intelligence
Security Analytics and Threat Intelligence
Advanced analytics transform raw security data into actionable intelligence. By applying statistical analysis, machine learning, and threat intelligence feeds, organizations can identify sophisticated attacks and predict future threats.
# Python security analytics platform
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import networkx as nx
from typing import List, Dict, Tuple
import asyncio
from datetime import datetime, timedelta
class SecurityAnalytics:
def __init__(self, data_store, threat_intel_client):
self.data_store = data_store
self.threat_intel = threat_intel_client
self.models = {}
self.initialize_models()
def initialize_models(self):
"""Initialize ML models for security analytics"""
# Anomaly detection model
self.models['anomaly_detector'] = IsolationForest(
contamination=0.01,
random_state=42
)
# Feature scaler
self.scaler = StandardScaler()
async def analyze_user_behavior(self, user_id: str,
time_window: timedelta = timedelta(days=30)):
"""Analyze user behavior for anomalies"""
# Fetch user activity data
end_time = datetime.utcnow()
start_time = end_time - time_window
activities = await self.data_store.get_user_activities(
user_id, start_time, end_time
)
if len(activities) < 10:
return None # Insufficient data
# Extract features
features = self.extract_user_features(activities)
# Detect anomalies
anomalies = self.detect_anomalies(features)
# Calculate risk score
risk_score = self.calculate_user_risk_score(
user_id, features, anomalies
)
return {
'user_id': user_id,
'risk_score': risk_score,
'anomalies': anomalies,
'behavior_profile': self.create_behavior_profile(features),
'recommendations': self.generate_recommendations(risk_score, anomalies)
}
def extract_user_features(self, activities: List[Dict]) -> pd.DataFrame:
"""Extract behavioral features from user activities"""
features = []
for activity in activities:
feature_vector = {
'hour_of_day': activity['timestamp'].hour,
'day_of_week': activity['timestamp'].weekday(),
'endpoint_entropy': self.calculate_endpoint_entropy(activity),
'request_rate': self.calculate_request_rate(activity),
'error_rate': self.calculate_error_rate(activity),
'data_volume': activity.get('response_size', 0),
'unique_ips': len(set(a.get('ip_address') for a in activities)),
'auth_failures': sum(1 for a in activities
if a.get('event_type') == 'auth_failure'),
'privilege_requests': sum(1 for a in activities
if 'admin' in a.get('endpoint', ''))
}
features.append(feature_vector)
return pd.DataFrame(features)
def detect_attack_patterns(self,
time_window: timedelta = timedelta(hours=1)):
"""Detect coordinated attack patterns"""
end_time = datetime.utcnow()
start_time = end_time - time_window
# Build activity graph
G = nx.DiGraph()
activities = self.data_store.get_activities(start_time, end_time)
for activity in activities:
# Add nodes
G.add_node(activity['ip_address'], type='ip')
G.add_node(activity['user_id'], type='user')
G.add_node(activity['endpoint'], type='endpoint')
# Add edges
G.add_edge(activity['ip_address'], activity['user_id'],
weight=1, timestamp=activity['timestamp'])
G.add_edge(activity['user_id'], activity['endpoint'],
weight=1, timestamp=activity['timestamp'])
# Detect suspicious patterns
patterns = []
# Pattern 1: Multiple users from same IP
for ip in [n for n in G.nodes() if G.nodes[n]['type'] == 'ip']:
users = list(G.successors(ip))
if len(users) > 5:
patterns.append({
'type': 'multiple_users_same_ip',
'ip': ip,
'users': users,
'severity': 'HIGH'
})
# Pattern 2: Rapid endpoint scanning
for user in [n for n in G.nodes() if G.nodes[n]['type'] == 'user']:
endpoints = list(G.successors(user))
if len(endpoints) > 20:
patterns.append({
'type': 'endpoint_scanning',
'user': user,
'endpoints_accessed': len(endpoints),
'severity': 'MEDIUM'
})
# Pattern 3: Coordinated activity
communities = nx.community.louvain_communities(G.to_undirected())
for community in communities:
if len(community) > 10:
patterns.append({
'type': 'coordinated_activity',
'entities': list(community),
'size': len(community),
'severity': 'HIGH'
})
return patterns
async def correlate_with_threat_intel(self, indicators: List[str]):
"""Correlate observed indicators with threat intelligence"""
threats = []
for indicator in indicators:
# Check IP reputation
if self.is_ip_address(indicator):
reputation = await self.threat_intel.check_ip_reputation(indicator)
if reputation['malicious']:
threats.append({
'indicator': indicator,
'type': 'ip',
'threat_level': reputation['threat_level'],
'categories': reputation['categories'],
'first_seen': reputation['first_seen'],
'last_seen': reputation['last_seen']
})
# Check domain reputation
elif self.is_domain(indicator):
reputation = await self.threat_intel.check_domain_reputation(indicator)
if reputation['malicious']:
threats.append({
'indicator': indicator,
'type': 'domain',
'threat_level': reputation['threat_level'],
'malware_families': reputation.get('malware_families', [])
})
return threats
def generate_security_report(self, time_period: str = 'daily'):
"""Generate comprehensive security report"""
report = {
'period': time_period,
'generated_at': datetime.utcnow().isoformat(),
'summary': {},
'top_threats': [],
'trends': {},
'recommendations': []
}
# Calculate summary statistics
report['summary'] = {
'total_requests': self.data_store.count_events('api_request'),
'failed_authentications': self.data_store.count_events(
'authentication',
filter={'success': False}
),
'authorization_denials': self.data_store.count_events(
'authorization',
filter={'granted': False}
),
'unique_attackers': len(self.get_unique_attackers()),
'blocked_ips': len(self.get_blocked_ips()),
}
# Identify top threats
report['top_threats'] = self.identify_top_threats()
# Analyze trends
report['trends'] = self.analyze_security_trends()
# Generate recommendations
report['recommendations'] = self.generate_recommendations_from_data()
return report
# Automated response system
class AutomatedSecurityResponse:
def __init__(self, security_analytics, response_config):
self.analytics = security_analytics
self.config = response_config
async def evaluate_and_respond(self, event: Dict):
"""Evaluate security event and execute appropriate response"""
# Calculate threat level
threat_level = await self.calculate_threat_level(event)
if threat_level >= self.config['auto_response_threshold']:
response_actions = self.determine_response_actions(event, threat_level)
for action in response_actions:
await self.execute_response_action(action, event)
async def execute_response_action(self, action: str, event: Dict):
"""Execute specific security response action"""
if action == 'block_ip':
duration = self.calculate_block_duration(event)
await self.security_gateway.block_ip(event['ip_address'], duration)
elif action == 'enable_captcha':
await self.security_gateway.enable_captcha_for_ip(event['ip_address'])
elif action == 'suspend_user':
await self.user_service.suspend_user(
event['user_id'],
reason='Automated security response'
)
elif action == 'increase_logging':
await self.logging_service.increase_verbosity(
user_id=event.get('user_id'),
ip_address=event.get('ip_address')
)
elif action == 'notify_security':
await self.notification_service.alert_security_team(event)
Effective monitoring and logging provide the visibility needed to maintain API security. The next chapter explores common API vulnerabilities and their remediation strategies.## Common API Security Vulnerabilities and Fixes
Understanding common API vulnerabilities is essential for building secure APIs. The OWASP API Security Top 10 provides a foundation, but real-world APIs face numerous additional threats. This chapter examines the most critical API vulnerabilities, demonstrates how attackers exploit them, and provides comprehensive remediation strategies with practical implementation examples.