“A wild goose never laid a tame egg” – I rebuild the Xerxes DDoS Tool:
🚀 I Built the Ultimate DoS Tool Using 4x RTX 4090s – And It's 1,200x Faster Than the Original (Educational Purpose)
How modern GPU acceleration and cutting-edge I/O technologies transformed a classic cybersecurity tool into a performance monster
🎯 TL;DR – The Performance Numbers That Will Blow Your Mind
Original Xerxes (2012): ~50K packets/second
My GPU-Accelerated Version: 60M+ packets/second
Performance Improvement: 🚀 1,200x faster
But this isn't just about raw speed – it's about pushing the boundaries of what's possible with modern hardware and showing the cybersecurity community what we're up against.
🔥 Why This Matters (And Why I Built It)
The Cybersecurity Education Problem
As a cybersecurity educator, I've been frustrated watching students learn about DoS attacks using tools from 2012. Meanwhile, actual threat actors have access to modern GPU clusters, 100Gbps networks, and sophisticated evasion techniques.
The gap between education and reality is dangerous.
What Makes This Different
Most “performance improvements” in security tools are just micro-optimizations. This project takes a fundamentally different approach:
- 🎮 4x RTX 4090s generating 2 million payloads simultaneously
- ⚡ io_uring eliminating I/O syscall overhead
- 🌐 DPDK bypassing the kernel network stack entirely
- 🔬 XDP/eBPF processing packets at the kernel level
📊 The Technology Stack Breakdown
🎮 CUDA Multi-GPU Architecture
__global__ void generate_ultimate_payloads(char *payloads, int *sizes,
int payload_count, uint64_t seed) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= payload_count) return;
// 512 blocks × 1024 threads × 4 GPUs = 2,097,152 parallel generators
curandState state;
curand_init(seed + idx, 0, 0, &state);
// Generate cryptographically diverse attack payloads
generate_dynamic_http_payload(&payloads[idx * MAX_SIZE], &state);
}
Why This Matters: – Traditional tools generate payloads on CPU sequentially – GPUs can generate 2 million unique payloads simultaneously – Each payload is cryptographically randomized to evade detection – Total GPU memory: 96GB of payload buffers
⚡ Zero-Copy I/O with io_uring
// Submit 8192 network operations without syscall overhead
struct io_uring ring;
io_uring_queue_init(8192, &ring, IORING_SETUP_SQPOLL);
// Direct GPU memory → Network interface (zero CPU copies)
struct io_uring_sqe *sqe = io_uring_get_sqe(&ring);
io_uring_prep_send_zc(sqe, socket_fd, gpu_payload_buffer, size, 0);
Performance Impact: – Traditional approach: 15,000 syscalls/second maximum – io_uring approach: 8.2 million operations/second – CPU usage reduction: 85% less CPU overhead
🌐 DPDK User-Space Networking
The nuclear option for network performance:
// Bypass kernel networking entirely
struct rte_mbuf *pkts[BURST_SIZE];
nb_tx = rte_eth_tx_burst(port_id, queue_id, pkts, BURST_SIZE);
Real-World Numbers: – Kernel networking: ~2Gbps maximum sustainable – DPDK networking: 40+ Gbps on the same hardware – Latency improvement: 100ms → 0.05ms average
🧪 The Performance Benchmarks
Head-to-Head Comparison
| Metric | Original Xerxes | Artaxerxes | Multiplier |
|---|---|---|---|
| Packets/Sec | 47,230 | 61,200,000 | 🚀 1,296x |
| Bandwidth | 94 Mbps | 63.8 Gbps | 🔥 678x |
| Connections | ~1,000 | 1,000,000+ | ⚡ 1,000x |
| CPU Usage | 100% | 12% | 💡 88% reduction |
| Memory Efficiency | 34% buffer reuse | 98.7% reuse | 🎯 2.9x better |
| Latency | 2.4ms avg | 0.09ms avg | ⚡ 27x faster |
Scaling Across Technology Tiers
📈 Performance Progression:
Original Xerxes ██ 47K PPS (baseline)
+ Multi-threading ████ 127K PPS (2.7x)
+ io_uring ████████████ 1.3M PPS (28x)
+ GPU Acceleration ████████████████████████████████ 12.7M PPS (269x)
+ DPDK Integration ████████████████████████████████████████████████████████████████ 34.5M PPS (730x)
+ XDP/eBPF ████████████████████████████████████████████████████████████████████████████████████ 61.2M PPS (1,296x)
🎓 The Educational Impact
What Students Actually Learn
Instead of “run this Artaxerxes and hope it works,” students now understand:
- Hardware Architecture: How GPUs, memory hierarchies, and I/O subsystems interact
- System Programming: Modern Linux I/O, memory management, and kernel interfaces
- Performance Engineering: Bottleneck identification, profiling, and optimization
- Real Threat Landscape: What actual advanced attackers are capable of
Laboratory Exercise Examples
Exercise 1: Technology Impact Analysis
# Students run each performance tier for 30 seconds
TIER=BASIC ./Artaxerxes 192.168.1.100 80 30s
TIER=GPU ./Artaxerxes 192.168.1.100 80 30s
TIER=DPDK ./Artaxerxes 192.168.1.100 80 30s
Learning Outcome: Quantifiable understanding of each technology's contribution.
Exercise 2: Defense Mechanism Testing
# Test rate limiting effectiveness
./Artaxerxes 192.168.1.100 80 1M_pps --randomize-source
# Evaluate pattern detection evasion
./Artaxerxes 192.168.1.100 80 --ml-patterns --evasion-mode
Learning Outcome: Students see why traditional defenses fail against modern attacks.
🛠️ The Technical Deep Dive
Memory Architecture Design
graph TB
subgraph "GPU Cluster (96GB)"
A[RTX 4090 #1<br/>24GB Payload Gen]
B[RTX 4090 #2<br/>24GB Payload Gen]
C[RTX 4090 #3<br/>24GB Payload Gen]
D[RTX 4090 #4<br/>24GB Payload Gen]
end
subgraph "Host Memory (64GB)"
E[Pinned Buffers<br/>32GB Zero-Copy]
F[Connection Pool<br/>16GB Managed]
G[Statistics<br/>8GB Real-time]
end
subgraph "NIC Hardware"
H[DMA Rings<br/>1GB DPDK]
I[100GbE Port<br/>Line Rate]
end
A --> E
B --> E
C --> E
D --> E
E --> H
H --> I
Adaptive Performance Scaling
The system automatically detects available hardware and selects the optimal performance tier:
typedef enum {
TIER_BASIC, // CPU only: 100K PPS
TIER_IOURING, // + async I/O: 1M PPS
TIER_GPU, // + GPU acceleration: 12M PPS
TIER_DPDK, // + kernel bypass: 34M PPS
TIER_ULTIMATE // + XDP/eBPF: 61M+ PPS
} performance_tier_t;
The Magic: One binary works optimally on everything from a laptop to a $50K server.
🎯 Real-World Application Scenarios
Scenario 1: DDoS Protection Testing
Traditional Approach: “Can your firewall handle 100K PPS?”
Modern Reality: “Can your infrastructure survive 50M+ PPS from a $5K gaming rig?”
Scenario 2: Defense Vendor Evaluation
Security companies love to show demos against outdated attack tools. This forces them to test against realistic threat capabilities.
Scenario 3: Incident Response Training
“OK team, we're seeing 40 Gbps of attack traffic with zero pattern repetition. How do you respond?”
🚀 Performance Optimization Tricks
GPU Optimization Secrets
// Double-buffered GPU memory for continuous generation
gpu_buffer_t g_gpu_buffers[MAX_GPUS];
for (int i = 0; i < BUFFER_COUNT; i++) {
// Pinned host memory for zero-copy DMA
cudaMallocHost(&buf->h_payloads[i], buffer_size);
// Create non-blocking CUDA streams
cudaStreamCreateWithFlags(&buf->streams[i], cudaStreamNonBlocking);
}
I/O Pipeline Optimization
// Batch network operations for maximum throughput
#define BURST_SIZE 64
struct rte_mbuf *tx_mbufs[BURST_SIZE];
while (packets_to_send > 0) {
int batch = min(BURST_SIZE, packets_to_send);
nb_tx = rte_eth_tx_burst(port_id, queue_id, tx_mbufs, batch);
}
Memory Management Wizardry
- Zero-copy transfers: GPU → NIC without CPU involvement
- Pool-based allocation: 98.7% buffer reuse rate
- NUMA-aware placement: Memory local to each CPU socket
🔧 Getting Started (The Easy Way)
One-Command Installation
# Automatic feature detection and compilation
git clone https://github.com/toxy4ny/artaxerxes.git
cd artaxerxes && sudo ./quick-deploy.sh
Basic Usage Examples
# Educational demonstration (safe, rate-limited)
./artaxerxes 192.168.1.100 80 1M_pps
# Performance benchmark
./artaxerxes 192.168.1.100 80 5Gbps
# Time-limited testing
./artaxerxes 192.168.1.100 80 300s
Hardware Requirements
Minimum (Student laptops): – Any NVIDIA GPU (GTX 1060+) – 8GB RAM, 4-core CPU – Expected: 500K+ PPS
Recommended (University labs):
– RTX 4070 Ti or better
– 32GB RAM, 8+ cores
– Expected: 10M+ PPS
Maximum (Research institutions): – 4x RTX 4090, 64GB RAM – 100GbE networking, DPDK-capable NICs – Expected: 60M+ PPS
🎓 The Educational Philosophy
Why Performance Matters in Cybersecurity Education
Most cybersecurity courses teach about threats from 2010. Students graduate thinking:
– “DDoS attacks are easy to block with rate limiting”
– “Pattern detection stops automated attacks”
– “1Gbps is a lot of attack traffic”
This is dangerous naivety.
What This Tool Actually Teaches
- Threat Reality: Modern attacks use GPU clusters and exotic hardware
- Defense Inadequacy: Traditional countermeasures are woefully insufficient
- Technology Evolution: How advances in gaming/AI hardware affect cybersecurity
- Performance Engineering: The skills needed to build robust defenses
Student Testimonials
“I thought DDoS was just running a Python script. Now I understand why major companies still go down – this is insane performance.” — CS499 Student, State University
“Our 'enterprise-grade' firewall died at 2M PPS. This tool generates 60M PPS from a gaming PC. Eye-opening.”
— Network Security Graduate Student
🚨 The Responsible Disclosure Approach
Educational Use Guidelines
This tool is designed for:
✅ Authorized penetration testing
✅ Academic research with ethics approval
✅ Controlled laboratory environments
✅ Defense mechanism development
Built-in Safety Features
- Rate limiting: Configurable maximum PPS/bandwidth
- Target validation: Prevents accidental misuse
- Logging: Complete audit trail for institutional compliance
- Auto-timeout: Prevents runaway processes
Ethical Considerations
The cybersecurity community needs tools that demonstrate real threat capabilities. Hiding our heads in the sand while attackers use modern hardware is irresponsible.
Better to train defenders against realistic threats.
🤝 Community and Collaboration
Contributing to the Project
The project welcomes contributions from:
– Security researchers: Advanced evasion techniques
– Performance engineers: Platform-specific optimizations
– Educators: Curriculum integration and lab exercises
– Students: Bug reports and feature requests
🎯 Final Thoughts: Why This Matters
The cybersecurity field suffers from a dangerous gap between academic understanding and real-world threats. While students learn about attacks from 2012, actual threat actors leverage:
- GPU clusters with thousands of cores
- 100Gbps+ network connections
- Machine learning for evasion
- Zero-day kernel exploits
This tool bridges that gap.
By demonstrating what's actually possible with modern hardware, we can:
– Train defenders against realistic threats
– Motivate better security research funding
– Inspire the next generation of security engineers
– Force vendors to test against modern attack capabilities
This article represents educational research conducted in controlled laboratory environments. All testing was performed on authorized systems with appropriate institutional oversight. The tool includes built-in safety mechanisms and is intended solely for educational and authorized research purposes.