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KL3FT3Z
KL3FT3Z

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"A wild goose never laid a tame egg" - I rebuild the Xerxes DDoS Tool

#cybersecurity #cuda #gpu #ddos

🚀 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);
}
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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);
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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);
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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)
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🎓 The Educational Impact

What Students Actually Learn

Instead of "run this Artaxerxes and hope it works," students now understand:

  1. Hardware Architecture: How GPUs, memory hierarchies, and I/O subsystems interact
  2. System Programming: Modern Linux I/O, memory management, and kernel interfaces
  3. Performance Engineering: Bottleneck identification, profiling, and optimization
  4. 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
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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
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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
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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;
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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);
}
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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);
}
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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
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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
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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

  1. Threat Reality: Modern attacks use GPU clusters and exotic hardware
  2. Defense Inadequacy: Traditional countermeasures are woefully insufficient
  3. Technology Evolution: How advances in gaming/AI hardware affect cybersecurity
  4. 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.

Top comments (2)

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gnomeman4201 profile image
GnomeMan4201 •

This tool is a rearmament. Major respect.
Also you’re clearly downplaying your depth and knowledge .The way you talk about this comes off humble, but from a technical standpoint, I think a lot of what you’ve done here flies over most developers’ heads .....not because it's obscure, but because it's built from a place most don’t operate from.... i'm inspired.

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toxy4ny profile image
KL3FT3Z •

the idea of the old xerxes stresser was written, in my opinion, back in the 90s, and the tool was quite powerful for its time and installed many servers around the world. My idea is to process code not in the processor and RAM of a computer, but in the GPU of a video card, the processor of which allows processing information and code tens of thousands of times faster than the processor and RAM. I am glad that my ideas inspire you, and as an author, I am pleased to hear this.