DDoS Attack Origin Analysis

When a DDoS attack hits your infrastructure, the immediate priority is mitigation — absorbing or filtering the malicious traffic to keep services running. But once the attack is contained, understanding where it came from, what infrastructure was used, and how the attack was orchestrated becomes critical for preventing the next one. IP intelligence transforms raw attack traffic logs into actionable intelligence, revealing the geographic distribution, network sources, and infrastructure behind DDoS campaigns.

The Problem

DDoS attacks are growing in both frequency and sophistication. According to Cloudflare’s DDoS threat report, the number of DDoS attacks continues to increase year over year, with attacks becoming more distributed and harder to attribute. A modern volumetric attack may involve hundreds of thousands of source IPs across dozens of countries, making manual analysis impossible.

After an attack, security teams are left with firewall logs containing millions of source IP addresses. Without enrichment, these IPs are just numbers. The team cannot answer basic questions: Was this a botnet of compromised residential devices or a coordinated attack from rented cloud infrastructure? Which countries and networks contributed the most traffic? Are the same source networks appearing in attacks against other organizations? Was the attack launched from known bulletproof hosting providers? IP intelligence answers all of these questions systematically.

How IP Intelligence Helps

IP enrichment adds geographic and network context to every source IP in your attack logs. By processing the attack traffic through the geolocation API, you can build a comprehensive profile of the attack infrastructure:

• Geographic distribution — map the attack sources by country, region, and city. This reveals whether the attack originated from a specific geographic cluster or was globally distributed, which indicates different attack methodologies.
• Network classification — determine whether attack traffic comes from residential ISPs (indicating a botnet of compromised home devices), datacenter IPs (indicating rented attack infrastructure), or mobile carriers (indicating compromised mobile devices).
• ASN analysis — identify which autonomous systems contributed the most attack traffic. A small number of ASNs generating a disproportionate volume of traffic often points to compromised hosting providers or bulletproof hosting services that tolerate abuse.
• Anonymization detection — flag attack sources using VPNs, proxies, or Tor to hide their origin. The proportion of anonymized traffic in an attack tells you whether the attacker is sophisticated enough to launder their traffic through anonymization layers.
• Historical correlation — compare attack source IPs against previous incidents. Overlap between attacks suggests a persistent threat actor reusing the same infrastructure, which may be attributable.

Key API Fields for DDoS Analysis

Post-Attack Analysis Workflow

After containing a DDoS attack, follow this workflow to extract maximum intelligence from the attack data:

Step 1: Export Attack Source IPs

Extract the unique source IP addresses from your DDoS mitigation platform, firewall logs, or flow data. A typical volumetric attack may have tens of thousands to hundreds of thousands of unique source IPs. Deduplicate the list and sort by traffic volume — the IPs contributing the most bandwidth are your primary targets for analysis.

Step 2: Bulk Enrich with IP Intelligence

Use the bulk lookup API to process the source IPs in batch. For each IP, the API returns geographic location, ASN, organization, ISP, connection type, and threat indicators. Store the enriched data in your SIEM or data warehouse for analysis.

// Bulk enrich DDoS source IPs
const sourceIPs = await extractAttackIPs(attackLog);
const enriched = await fetch('https://api.myiphelp.com/v1/bulk', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({ ips: sourceIPs })
}).then(r => r.json());

// Aggregate by ASN
const asnBreakdown = enriched.reduce((acc, ip) => {
  acc[ip.asn] = (acc[ip.asn] || 0) + ip.traffic_volume;
  return acc;
}, {});

Step 3: Build the Attack Profile

Aggregate the enriched data to build a comprehensive attack profile. The key dimensions to analyze are the geographic distribution (which countries contributed what percentage of traffic), the network breakdown (residential botnet vs datacenter infrastructure), the top ASNs by traffic volume, and the proportion of anonymized traffic (VPN, proxy, Tor). This profile tells you what type of attack you faced and what infrastructure the attacker used.

Step 4: Generate Defensive Intelligence

Transform the attack profile into actionable defenses. Create blocklists of the top-contributing ASNs and IP ranges. Update your rate limiting rules based on the geographic distribution. If the attack was primarily from datacenter IPs, tighten your datacenter IP filtering. If it was from residential IPs, work with your DDoS mitigation provider to update their behavioral detection rules. Share the intelligence with industry peers through threat sharing platforms like the MISP Threat Intelligence Platform to help others defend against the same infrastructure.

Attack Pattern Recognition

Different types of DDoS attacks produce distinct IP intelligence signatures:

• IoT botnets (Mirai-style) — attack sources are predominantly residential ISPs and mobile carriers across many countries. The geographic distribution is highly diverse, with significant traffic from regions with large numbers of insecure IoT devices. Connection types are overwhelmingly residential.
• Booter/stresser services — attack sources concentrate in a small number of datacenter ASNs, often associated with hosting providers that have weak abuse policies. The geographic distribution clusters around countries with inexpensive hosting. VPN and proxy flags may be elevated.
• Amplification attacks (DNS, NTP, memcached) — source IPs are legitimate servers running misconfigured services. These are typically datacenter IPs with valid reverse DNS and established organizations. The key indicator is that these IPs are not malicious themselves — they are being exploited as amplifiers by spoofed requests.
• Application-layer attacks — source IPs often come from datacenter and cloud provider ranges, using rotating proxies or VPNs to bypass rate limiting. The is_datacenter and is_proxy flags are the primary detection signals. These attacks are harder to detect because each individual source sends traffic that looks legitimate.

Why My IP Help

• Bulk processing — analyze hundreds of thousands of attack source IPs in a single batch request. Essential for post-attack forensics where manual IP-by-IP lookup is impractical.
• Network-level classification — distinguish residential botnet traffic from rented datacenter infrastructure. This distinction is critical for understanding the attack type and choosing the right defensive response.
• Threat scoring — prioritize which IPs to add to permanent blocklists based on composite threat scores. Not every attack source deserves a permanent block — many residential IPs are temporarily compromised and will be cleaned up.
• Real-time and forensic modes — use real-time lookups during active attacks for immediate blocking decisions, and bulk lookups afterward for comprehensive forensic analysis.

Frequently Asked Questions