Monitoring and Alerting

Continuous monitoring transforms firewalls from static defenses into dynamic security tools. Effective monitoring strategies provide visibility into attack patterns, performance impacts, and potential misconfigurations.

Log Aggregation and Analysis: Centralize firewall logs for comprehensive analysis:

# Log aggregation pipeline
import json
from elasticsearch import Elasticsearch
from datetime import datetime

es = Elasticsearch(['localhost:9200'])

def process_firewall_log(log_entry):
    # Parse log entry
    parsed = {
        'timestamp': datetime.now(),
        'source_ip': log_entry.get('src'),
        'destination_ip': log_entry.get('dst'),
        'destination_port': log_entry.get('dpt'),
        'action': log_entry.get('action'),
        'rule_id': log_entry.get('rule_id')
    }
    
    # Enrich with GeoIP data
    if parsed['source_ip']:
        parsed['geoip'] = get_geoip_data(parsed['source_ip'])
    
    # Index in Elasticsearch
    es.index(
        index=f"firewall-logs-{datetime.now().strftime('%Y.%m.%d')}",
        body=parsed
    )
    
    # Check for alert conditions
    if should_alert(parsed):
        send_alert(parsed)

def should_alert(log_entry):
    # Alert on high-frequency blocks from single IP
    recent_blocks = es.count(
        index="firewall-logs-*",
        body={
            "query": {
                "bool": {
                    "must": [
                        {"term": {"source_ip": log_entry['source_ip']}},
                        {"term": {"action": "block"}},
                        {"range": {"timestamp": {"gte": "now-5m"}}}
                    ]
                }
            }
        }
    )
    
    return recent_blocks['count'] > 100

Key Performance Indicators: Track metrics that indicate firewall health and effectiveness:

Connection Rate: New connections per second
Rule Hit Rate: Frequency of each rule matching
Processing Latency: Time added by firewall processing
Drop Rate: Percentage of traffic blocked
Resource Utilization: CPU, memory, and connection table usage

Automated Response: Implement automated responses to common scenarios:

# Automated threat response
class FirewallAutomation:
    def __init__(self, firewall_api):
        self.firewall = firewall_api
        self.threat_threshold = 1000
        
    def analyze_threats(self, time_window=300):
        # Get recent blocked IPs
        blocked_ips = self.firewall.get_blocked_ips(time_window)
        
        for ip, count in blocked_ips.items():
            if count > self.threat_threshold:
                self.escalate_block(ip)
    
    def escalate_block(self, ip):
        # Add to long-term blocklist
        self.firewall.add_to_blocklist(ip, duration=86400)
        
        # Update threat intelligence
        self.update_threat_intel(ip)
        
        # Notify security team
        self.send_notification(f"IP {ip} added to 24-hour block")