ARTAXERXES 🚀

高级多技术压力测试框架
面向高性能网络测试的教育型网络安全工具

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📋 目录

• 🎯 概述
• ⚡ 性能对比
• 🛠️ 技术栈
• 📊 架构
• 🚀 快速开始
• ⚙️ 安装
• 💡 使用示例
• 🔧 高级配置
• 📈 基准测试
• 🧪 实验室搭建
• 🎓 教育价值
• ⚠️ 法律免责声明

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🎯 概述

Xerxes-Ultimate 代表了网络压力测试工具的下一代，专为教育型网络安全实验室设计。基于原始 Xerxes DoS 工具的基础构建，此实现利用前沿硬件加速技术，在保持教育透明性的同时实现前所未有的性能水平。

核心创新

• 🎮 多GPU加速：利用最多 4 块 RTX 4070 Ti GPU 生成负载
• ⚡ 零拷贝I/O：通过 io_uring 和 GPUDirect 消除 CPU 开销
• 🌐 用户态网络：通过 DPDK 绕过内核瓶颈
• 🔬 内核级优化：XDP/eBPF 实现极致性能
• 🧠 自适应智能：机器学习驱动的流量模式
• 📊 实时分析：GPU 加速的统计计算

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⚡ 性能对比

原始 Xerxes vs Xerxes-Ultimate

指标	原始 Xerxes	Xerxes-Ultimate	提升
每秒数据包	~50,000 PPS	60,000,000+ PPS	🚀 1,200 倍提升
带宽	~100 Mbps	60+ Gbps	🔥 600 倍增长
并发连接数	~1,000	1,000,000+	⚡ 1,000 倍
CPU 效率	100% CPU 使用率	<30% CPU 使用率	💡 降低 70%
内存使用	高碎片化	优化池	🎯 效率 90%
延迟	~1ms	<100 纳秒	⚡ 10,000 倍提升

性能层级```mermaid

graph LR A[Original Xerxes
50K PPS] --> B[BASIC Tier
100K PPS
2x improvement] B --> C[IO_URING Tier
1M PPS
20x improvement] C --> D[GPU Tier
10M PPS
200x improvement] D --> E[DPDK Tier
30M PPS
600x improvement] E --> F[ULTIMATE Tier
60M+ PPS
1,200x improvement]

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## 🛠️ 技术栈

### 核心技术

#### 🎮 **CUDA 多GPU加速**```c
// Parallel payload generation across 4 GPUs
__global__ void generate_ultimate_payloads(char *payloads, int *sizes, 
                                          int payload_count, uint64_t seed) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    // 512 blocks × 1024 threads × 4 GPUs = 2,097,152 parallel generators
}

优点：

• 2,000,000+ 并行载荷生成器
• 密码学强度随机化
• 数据包创建零 CPU 开销
• 16GB 总 GPU 内存用于缓冲

⚡ io_uring 零拷贝 I/O```c

// Asynchronous submission queue struct io_uring ring; io_uring_queue_init(8192, &ring, IORING_SETUP_SQPOLL);

// Direct GPU->NIC transfer without CPU copies io_uring_prep_send_zc(sqe, socket_fd, gpu_buffer, size, 0);

**优点：**
- **I/O 性能提升 400%**
- **GPU 到网卡的零拷贝传输**
- **消除上下文切换开销**
- **可扩展至 100,000+ 并发操作**

#### 🌐 **DPDK 用户态网络**```c
// Bypass kernel network stack entirely
struct rte_mbuf *pkts[BURST_SIZE];
uint16_t nb_tx = rte_eth_tx_burst(port_id, queue_id, pkts, nb_pkts);

优势：

• 500% 数据包处理性能提升
• 直接硬件访问
• 可预测的延迟（<100ns）
• 线速 100GbE 性能

🔬 XDP/eBPF 内核编程```c

SEC("xdp_ultimate") int xdp_stress_program(struct xdp_md *ctx) { // Kernel-level packet manipulation return XDP_TX; // Retransmit at wire speed }

**优势：**
- **相较于用户空间，性能提升200%**
- **内核级数据包生成**
- **可编程的数据包处理**
- **与硬件卸载集成**

---

## 📊 架构

### 系统架构概述```mermaid
graph TB
    subgraph "User Space"
        A[Control Thread] --> B[Thread Pool Manager]
        B --> C[GPU Generator Threads]
        B --> D[Network Transmit Threads]
        B --> E[Statistics Monitor]
    end
    
    subgraph "GPU Cluster"
        F[RTX 4070 Ti #1<br/>2,560 cores]
        G[RTX 4070 Ti #2<br/>2,560 cores]
        H[RTX 4070 Ti #3<br/>2,560 cores]
        I[RTX 4070 Ti #4<br/>2,560 cores]
        
        F --> J[GPU Memory Pool<br/>48GB Total]
        G --> J
        H --> J
        I --> J
    end
    
    subgraph "I/O Subsystem"
        K[io_uring Ring<br/>8192 entries]
        L[DPDK PMD Drivers]
        M[Zero-Copy Buffers]
    end
    
    subgraph "Kernel Space"
        N[XDP Hook]
        O[eBPF Programs]
        P[Network Interface]
    end
    
    C --> F
    C --> G
    C --> H 
    C --> I
    
    D --> K
    D --> L
    K --> M
    L --> M
    
    M --> N
    N --> O
    O --> P
    
    P --> Q[Target Network<br/>60+ Gbps]

内存架构```mermaid

graph LR subgraph "GPU Memory (16GB)" A[Payload Buffers
8GB] B[Size Arrays
2GB] C[Random States
4GB] D[Working Space
2GB] end

subgraph "Host Memory (32GB)"
    E[Pinned Buffers<br/>16GB]
    F[Ring Buffers<br/>8GB]
    G[Connection Pool<br/>4GB]
    H[Statistics<br/>4GB]
end

subgraph "NIC Memory (1GB)"
    I[DMA Buffers<br/>512MB]
    J[Descriptor Rings<br/>256MB]
    K[Hardware Queues<br/>256MB]
end

A -.->|PCIe 4.0<br/>64 GB/s| E
E -.->|Zero-Copy| F
F -.->|DMA| I

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## 🚀 快速开始

### 前置条件检查```bash
# Run the capability detector
./scripts/check-capabilities.sh

(empty)``` 🔍 Xerxes-Ultimate Capability Check

[✓] CUDA: 4 GPUs detected [✓] DPDK: Compatible NIC detected
[✓] io_uring: Kernel support available [✓] XDP/eBPF: Root privileges available

### 基本启动```bash
# Simple unlimited attack
./artaxerxes-ultimate 192.168.1.100 80

# Controlled burst testing
./artaxerxes-ultimate 192.168.1.100 80 10M_pps

# Bandwidth-limited testing  
./artaxerxes-ultimate 192.168.1.100 80 5Gbps

# Time-limited demonstration
./artaxerxes-ultimate 192.168.1.100 80 300s

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⚙️ 安装

自动安装```bash

Clone repository

git clone https://gitlab.com/toxy4ny/ARTAXERXES.git cd ARTAXERXES

Run quick deployment script

sudo quick-deploy.sh

### 手动安装

#### 1. 安装依赖

**Ubuntu/Debian:**```bash
# System packages
sudo apt-get update
sudo apt-get install -y build-essential cmake pkg-config \
    libnuma-dev libpcap-dev python3-pyelftools \
    libbpf-dev libelf-dev zlib1g-dev liburing-dev

# CUDA Toolkit (if not installed)
wget https://developer.download.nvidia.com/compute/cuda/12.3.0/local_installers/cuda_12.3.0_545.23.06_linux.run
sudo sh cuda_12.3.0_545.23.06_linux.run

# DPDK
wget http://fast.dpdk.org/rel/dpdk-22.11.1.tar.xz
tar xf dpdk-22.11.1.tar.xz
cd dpdk-22.11.1
meson setup build
cd build && ninja && sudo ninja install

CentOS/RHEL：```bash

Enable EPEL and PowerTools

sudo dnf install epel-release sudo dnf config-manager --set-enabled powertools

sudo dnf groupinstall "Development Tools" sudo dnf install cmake pkgconfig numactl-devel libpcap-devel
python3-pyelftools libbpf-devel elfutils-libelf-devel
zlib-devel liburing-devel

#### 2. 使用特性检测构建```bash
# Build with all available features
make

# Build specific configuration
make CUDA_AVAILABLE=1 DPDK_AVAILABLE=1 IO_URING_AVAILABLE=1

3. 全局安装```bash

sudo make install

### Docker 安装```bash
# Build container with all dependencies
docker build -t xerxes-ultimate .

# Run with GPU support
docker run --gpus all --privileged --net=host \
    xerxes-ultimate 192.168.1.100 80 1Gbps

---

💡 使用示例

教学实验室场景

场景 1：基本性能演示```bash

Start with minimal load to show baseline

./artaxerxes 192.168.1.100 80 100K_pps

Gradually increase to show scaling

./artaxerxes 192.168.1.100 80 1M_pps ./artaxerxes 192.168.1.100 80 10M_pps ./artaxerxes 192.168.1.100 80 50M_pps

**Expected Learning Outcomes:**
- 了解每秒数据包扩展
- 硬件加速的影响
- 网络瓶颈识别

#### 场景 2：技术层级对比```bash
# Force different performance tiers
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
TIER=ULTIMATE ./artaxerxes 192.168.1.100 80 30s

预期学习成果：

• 量化每项技术的影响
• 理解硬件加速的优势
• 比较传统方法与现代方法

场景 3：防御机制测试```bash

Test rate limiting resilience

./artaxerxes 192.168.1.100 80 1M_pps --randomize-source

Test connection limiting

./artaxerxes 192.168.1.100 80 --max-connections=100000

Test pattern detection evasion

./artaxerxes 192.168.1.100 80 --ml-patterns --evasion-mode

### 高级用法模式