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Introduction:
Modern distributed denial-of-service (DDoS) attacks have evolved beyond raw bandwidth floods. Attackers now use “pulse” patterns—bursts of traffic just below detection thresholds—to reset rate‑limit counters and evade traditional defenses. This article dissects a real‑world attack that generated 2.45 billion requests from 1.2 million unique IPs across 16,000+ autonomous systems, and why adaptive, real‑time behavioural analysis is the only way to stop it.
Learning Objectives:
- Understand how pulse‑based DDoS attacks exploit rate‑limiting counter resets and blend traffic through legitimate cloud providers.
- Learn to analyse ASN (Autonomous System Number) distributions and identify malicious patterns using OSINT and BGP tools.
- Implement adaptive rate limiting, machine learning anomaly detection, and cloud hardening against low‑and‑slow volumetric threats.
You Should Know:
1. Decoding the Pulse Attack: Evasion by Design
In this attack, the adversary sent bursts of requests for a few seconds, then paused—allowing rate‑limit counters to reset—before repeating. Each burst stayed under typical thresholds (e.g., 100 requests/second per IP). Over 5 hours, cumulatively, the traffic reached 2.45 billion requests. This “low‑and‑slow” method mimics legitimate user spikes while exhausting server resources.
Step‑by‑step guide to simulate and detect pulse attacks:
- Linux (simulation with hping3):
Send 50 packets in a 0.5s burst, then sleep 10s (reset window) for i in {1..1000}; do sudo hping3 -S -p 80 --flood --rand-source -c 50 target.com; sleep 10; done - Monitor rate‑limit resets (Linux – watch Nginx logs):
tail -f /var/log/nginx/access.log | awk '{print $1}' | uniq -c | while read count ip; do if [ $count -gt 80 ]; then echo "Potential burst from $ip: $count requests"; fi; done - Windows PowerShell – detect repeated bursts in IIS logs:
Get-Content C:\inetpub\logs\LogFiles\W3SVC1\u_ex.log | Select-String "GET" | Group-Object { $<em>.Split()[bash] } | Where-Object { $</em>.Count -gt 100 -and $_.Count -lt 150 } | Format-Table Name, CountWhat this does: The Linux loop sends short bursts below most default (100 req/sec) rate limits. The monitoring scripts flag IPs with repeated burst patterns, not just high volume.
2. ASN Fingerprinting: Spotting Malicious Autonomous Systems
The attacker used 16,000+ ASNs, including privacy‑focused and legitimate cloud providers (e.g., VPN gateways, free CDNs). Traditional IP blacklists fail because each source IP contributes only a few requests. Analysing ASN distribution reveals coordinated attacks.
Step‑by‑step to map and block malicious ASNs:
- Query ASN for an IP (Linux):
whois -h whois.cymru.com " -v 203.0.113.45" Returns ASN, name, country
- Bulk analysis – extract top attack ASNs from logs:
awk '{print $1}' access.log | sort -u | while read ip; do whois -h whois.cymru.com " -v $ip" 2>/dev/null; done | sort | uniq -c | sort -nr | head -20 - Windows (using PowerShell + external API):
$ips = Get-Content attack_ips.txt; foreach ($ip in $ips) { Invoke-RestMethod "https://stat.ripe.net/data/geoloc/data.json?resource=$ip" | Select-Object -ExpandProperty data | Select -ExpandProperty located_resources }Mitigation: Create firewall rules to throttle or block entire ASNs that show only attack traffic (e.g.,
iptables -A INPUT -s 192.0.2.0/24 -m limit --limit 10/second -j ACCEPT).
3. Adaptive Rate Limiting: Moving Beyond Static Thresholds
Static rate limits (e.g., 100 req/min per IP) fail against distributed pulse attacks. Adaptive sliding window counters that adjust based on global request velocity and historical baselines are required.
Implementation example using Nginx + Lua (OpenResty):
-- Dynamic rate limit: if global requests exceed 10k/sec, drop to 20 req/min per IP
local glob_rps = ngx.shared.global:incr("rps", 1, 0)
if glob_rps > 10000 then
local key = ngx.var.binary_remote_addr
local limit = ngx.shared.dyn_limit
local req, _ = limit:get(key)
if req and req > 20 then
ngx.exit(429)
else
limit:incr(key, 1, 0)
end
end
Step‑by‑step to deploy:
1. Install OpenResty (`sudo apt install openresty`).
- Add `lua_shared_dict global 10m; lua_shared_dict dyn_limit 10m;` to
nginx.conf. - Place the above Lua snippet in `location /` block.
4. Reload Nginx: `sudo systemctl reload openresty`.
4. Machine Learning for Real‑Time Anomaly Detection
Traditional signature‑based WAFs miss pulse DDoS. ML models trained on request intervals, entropy of source IPs, and ASN cardinality can flag attacks within seconds.
Tutorial – Isolation Forest for traffic anomaly detection (Python):
import numpy as np
from sklearn.ensemble import IsolationForest
Features: request_rate, interval_variance, unique_ASNs_per_minute, burst_frequency
X = np.array([[120, 0.05, 45, 0.3], [5000, 0.9, 1200, 0.8], ...]) normal vs attack
model = IsolationForest(contamination=0.1, random_state=42)
model.fit(X)
Predict live traffic sample
if model.predict([[4500, 0.85, 950, 0.75]])[bash] == -1:
print("DDoS pattern detected – trigger rate limit")
Integration: Expose as REST API (FastAPI) and call from WAF middleware. For production, use streaming frameworks like Apache Flink.
5. Cloud Hardening Against Volumetric Attacks
Cloud providers offer DDoS mitigation, but default configurations miss pulse attacks. Hardening includes leveraging edge rate limiting and flow telemetry.
Step‑by‑step for AWS Shield Advanced + WAF:
- Enable AWS WAF with rate‑based rule: “Block IPs exceeding 100 requests per 30 seconds” – but also add a custom condition on `req_header` “X-Forwarded-For” counting unique values.
- Create a Lambda function that monitors CloudWatch metrics for `DDoSDetected` and dynamically lowers thresholds.
- Use VPC Flow Logs to detect unexpected ASNs:
-- Athena query SELECT dstaddr, count() as requests FROM vpc_flow_logs WHERE action='ACCEPT' AND srcaddr NOT IN (SELECT allowed_ips FROM whitelist) GROUP BY dstaddr HAVING count() > 10000
- Deploy AWS Global Accelerator to absorb traffic at edge.
-
API Security: Token Bucket vs. Leaky Bucket for Distributed Sources
Pulse attacks often target APIs. A centrally coordinated token bucket (using Redis) is more resilient than per‑IP leaky buckets.
Redis‑based token bucket in Node.js:
const redis = require('redis');
const client = redis.createClient();
async function rateLimit(userId) {
let tokens = await client.get(<code>tokens:${userId}</code>);
if (!tokens) tokens = 100; // refill 100 tokens
if (tokens < 1) return false;
await client.decr(<code>tokens:${userId}</code>);
return true;
}
// Refill at 20 tokens/sec
setInterval(() => { client.keys('tokens:', (err, keys) => keys.forEach(k => client.incrby(k, 20))); }, 1000);
Why this works: The centralized token bucket shares state across all edge servers, preventing attackers from resetting limits by rotating IPs.
- Incident Response: Simulating a Pulse DDoS for Drills
To test defenses, simulate a realistic attack using open‑source tools inside a lab environment (e.g., Kali Linux attacking a sandboxed Nginx server).
Step‑by‑step drill setup:
- Target: Deploy a vanilla Nginx with default rate limiting (
limit_req_zone $binary_remote_addr zone=one:10m rate=10r/s). - Attacker (Kali): Use `slowloris` to simulate low‑rate pulses:
git clone https://github.com/gkbrk/slowloris.git; cd slowloris; ./slowloris.py -p 80 -s 50 -t 600 target-ip
(Note: Only in isolated lab.)
- Detection: Monitor `limit_req` logs – you’ll see many “delaying request” entries but no full block because the default threshold isn’t crossed.
- Adaptive mitigation: Deploy the Lua script from section 3 – now the drill traffic triggers the dynamic 20 req/min limit, demonstrating effectiveness.
What Undercode Say:
- Pulse DDoS attacks are becoming the new normal; static rate limits and IP blacklists are obsolete against 1.2 million rotating IPs.
- ASN‑level throttling and machine learning on request entropy are required to catch attacks that deliberately stay “under the radar.”
- The DataDome case shows that real‑time behavioural analysis (not just volume) is the only defense that scales.
- Cloud native tools (Shield, WAF, Flow Logs) must be augmented with custom adaptive logic.
- Red‑teaming pulse attacks in your own lab is critical – you cannot defend what you do not simulate.
- Organisations should shift from “blocking high volume” to “slowing distributed bursts” as a primary strategy.
- Rate‑limit counters must be centralised (Redis) not per‑edge to prevent reset exploits.
- The cost of a successful pulse DDoS is not just downtime – it’s application exhaustion and degraded performance for legitimate users.
- Training courses and certifications (e.g., SANS SEC541, AWS Security Specialty) now explicitly cover pulse and low‑and‑slow attack vectors.
- Expect future attacks to combine pulse DDoS with application‑layer exploits (e.g., slow POST, hash collisions) for compounded damage.
Prediction:
By 2026, over 60% of DDoS attacks will use pulse or “low‑and‑slow” patterns, rendering legacy scrubbing centres ineffective. Defence will shift entirely to edge‑based adaptive AI models that learn per‑endpoint baselines in real time. Organisations that fail to implement dynamic rate limiting and ASN reputation scoring will face prolonged “degraded availability” incidents rather than binary up/down outages. The next frontier will be using generative AI to predict attack pulses before they start, based on early reconnaissance patterns in DNS and BGP routing changes.
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