Distributed Denial of Service (DDoS) attacks continue to evolve in both scale and complexity. For developers and infrastructure operators running public-facing services—especially game servers and APIs—basic firewall rules are no longer sufficient.
This article outlines a practical approach to building a high-performance mitigation pipeline using Linux-native technologies such as nftables and XDP. The concepts presented here are based on real-world implementations used at ArzenLabs.
Problem Overview
Typical attack patterns observed in production environments include:
High packet-rate UDP floods targeting open service ports
Amplification attacks using spoofed sources
Burst traffic designed to exhaust connection tracking
These attacks aim to overwhelm network handling capacity rather than exploit application logic.
Architecture Overview
An effective mitigation pipeline should operate across multiple layers:
Early packet drop (XDP / eBPF)
Kernel-level filtering (nftables)
Dynamic reputation-based blocking
Upstream filtering (provider-level)
Each layer reduces load progressively, ensuring system stability under attack conditions.
Layer 1: Early Drop with XDP
XDP (eXpress Data Path) allows packet filtering at the NIC level, before the kernel network stack is fully engaged.
Example Concept
Drop invalid or malformed packets immediately
Filter obvious flood patterns before conntrack involvement
Pseudo-logic:
if (udp_packet && packet_rate_exceeds_threshold) {
return XDP_DROP;
}
Why XDP Matters
Extremely low latency filtering
Prevents CPU exhaustion
Handles high packet-per-second (PPS) attacks efficiently
Layer 2: nftables Rate Limiting
After initial filtering, nftables can enforce structured rules.
Basic Rate Limit Rule
nft add table inet ddos
nft add chain inet ddos input { type filter hook input priority 0 \; }
nft add rule inet ddos input udp dport 25565 limit rate 300/second burst 600 packets accept
nft add rule inet ddos input udp dport 25565 drop
Key Behavior
Accepts normal traffic within defined thresholds
Drops excessive packets automatically
Reduces impact of volumetric floods
Layer 3: Dynamic Blacklisting
Static rules are insufficient against distributed attacks. A dynamic system is required.
Example Setup
nft add set inet ddos blacklist { type ipv4_addr\; flags timeout\; }
nft add rule inet ddos input ip saddr @blacklist drop
Logic
Detect abusive IPs based on rate thresholds
Add them to a temporary blacklist
Automatically expire entries after timeout
Layer 4: Upstream Mitigation
Local filtering alone cannot handle large-scale attacks. Upstream protection is essential.
Typical strategies include:
Provider-level firewalls
Traffic scrubbing centers
Anycast-based distribution
This layer absorbs the bulk of volumetric attacks before they reach the server.
Performance Considerations
When designing mitigation systems, consider:
Packet-per-second (PPS) limits rather than bandwidth alone
CPU overhead of filtering rules
Impact of conntrack on high-volume UDP traffic
Optimizing early-drop mechanisms significantly improves system resilience.
Common Mistakes
Relying solely on iptables without rate limiting
Enabling conntrack for all UDP traffic
Not isolating backend services from direct exposure
Ignoring monitoring and observability
Practical Outcome
A properly designed pipeline:
Reduces attack surface significantly
Maintains service availability under load
Minimizes latency impact for legitimate users
Conclusion
DDoS mitigation is not achieved through a single tool or rule set. It requires a layered architecture that combines early packet filtering, kernel-level enforcement, and upstream protection.
The approach outlined here reflects how modern infrastructure teams build resilient systems capable of handling high-volume attacks in production environments.
At ArzenLabs, the focus remains on engineering