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feat: v2.1.0 核心重构与功能增强

Browse Source 
## 架构重构
- 全局变量消除，迁移至 Config/State 对象
- SMB 插件融合（smb/smb2/smbghost/smbinfo）
- 服务探测重构，实现 Nmap 风格 fallback 机制
- 输出系统重构，TXT 实时刷盘 + 双写机制
- i18n 框架升级至 go-i18n

## 性能优化
- 正则表达式预编译
- 内存优化 map[string]struct{}
- 并发指纹匹配
- SOCKS5 连接复用
- 滑动窗口调度 + 自适应线程池

## 新功能
- Web 管理界面
- 多格式 POC 适配（xray/afrog）
- 增强指纹库（3139条）
- Favicon hash 指纹识别
- 插件选择性编译（Build Tags）
- fscan-lab 靶场环境
- 默认端口扩展（62→133）

## 构建系统
- 添加 no_local tag 支持排除本地插件
- 多版本构建：fscan/fscan-nolocal/fscan-web
- CI 添加 snapshot 模式支持仅测试构建

## Bug 修复
- 修复 120+ 个问题，包括 RDP panic、批量扫描漏报、
  JSON 输出格式、Redis 检测、Context 超时等

## 测试增强
- 单元测试覆盖率 74-100%
- 并发安全测试
- 集成测试（Web/端口/服务/SSH/ICMP）
This commit is contained in:
ZacharyZcR
2026-01-11 20:16:23 +08:00
parent 6b13b2e84f
commit 71b92d4408
948 changed files with 92335 additions and 24630 deletions
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tools/perftest/perftest.go
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// perftest - fscan 可扩展性测试工具
// 测量不同线程数下的扫描性能，生成 CSV 数据用于绘图
package main
import (
"encoding/csv"
"flag"
"fmt"
"os"
"os/exec"
"regexp"
"strconv"
"strings"
"time"
)
type Result struct {
Threads int
Duration float64 // 秒
PortsRate float64 // ports/sec
FailRate float64 // 失败率%
}
func main() {
target := flag.String("target", "", "扫描目标 (如 192.168.1.0/24)")
ports := flag.String("ports", "22,80,443,3389,8080", "端口列表")
threads := flag.String("threads", "100,200,400,600,800,1000", "线程数列表，逗号分隔")
repeat := flag.Int("repeat", 3, "每个线程数重复次数")
output := flag.String("o", "perf_results.csv", "输出CSV文件")
flag.Parse()
if *target == "" {
fmt.Println("用法: perftest -target 192.168.1.0/24 [-ports 22,80,443] [-threads 100,200,400]")
os.Exit(1)
}
threadList := parseIntList(*threads)
results := []Result{}
fmt.Printf("=== fscan 可扩展性测试 ===\n")
fmt.Printf("目标: %s\n", *target)
fmt.Printf("端口: %s\n", *ports)
fmt.Printf("线程数: %v\n", threadList)
fmt.Printf("重复次数: %d\n\n", *repeat)
for _, t := range threadList {
var totalDuration float64
var totalRate float64
fmt.Printf("[线程=%d] ", t)
for i := 0; i < *repeat; i++ {
fmt.Printf(".")
duration, rate := runFscan(*target, *ports, t)
totalDuration += duration
totalRate += rate
}
avgDuration := totalDuration / float64(*repeat)
avgRate := totalRate / float64(*repeat)
results = append(results, Result{
Threads: t,
Duration: avgDuration,
PortsRate: avgRate,
})
fmt.Printf(" 平均: %.2fs, %.1f ports/sec\n", avgDuration, avgRate)
}
writeCSV(*output, results)
fmt.Printf("\n结果已保存到: %s\n", *output)
printPlotCommand(*output)
}
func runFscan(target, ports string, threads int) (duration float64, rate float64) {
args := []string{
"-h", target,
"-p", ports,
"-t", strconv.Itoa(threads),
"-np", "-nopoc", // 禁用ping和poc，只测端口扫描
"-o", "/dev/null",
}
start := time.Now()
cmd := exec.Command("./fscan", args...)
output, _ := cmd.CombinedOutput()
duration = time.Since(start).Seconds()
// 从输出解析扫描的端口数
portCount := extractPortCount(string(output), target, ports)
if duration > 0 {
rate = float64(portCount) / duration
}
return
}
func extractPortCount(output, target, ports string) int {
// 尝试从 "扫描完成" 行提取
re := regexp.MustCompile(`扫描完成.*?(\d+).*?端口`)
if matches := re.FindStringSubmatch(output); len(matches) > 1 {
count, _ := strconv.Atoi(matches[1])
return count
}
// 估算: IP数 × 端口数
ipCount := estimateIPCount(target)
portCount := len(strings.Split(ports, ","))
return ipCount * portCount
}
func estimateIPCount(target string) int {
if strings.Contains(target, "/24") {
return 254
}
if strings.Contains(target, "/16") {
return 65534
}
return 1
}
func parseIntList(s string) []int {
parts := strings.Split(s, ",")
result := make([]int, 0, len(parts))
for _, p := range parts {
if n, err := strconv.Atoi(strings.TrimSpace(p)); err == nil {
result = append(result, n)
}
}
return result
}
func writeCSV(filename string, results []Result) {
f, err := os.Create(filename)
if err != nil {
fmt.Printf("无法创建文件: %v\n", err)
return
}
defer func() { _ = f.Close() }()
w := csv.NewWriter(f)
_ = w.Write([]string{"threads", "duration_sec", "ports_per_sec"})
for _, r := range results {
_ = w.Write([]string{
strconv.Itoa(r.Threads),
fmt.Sprintf("%.3f", r.Duration),
fmt.Sprintf("%.1f", r.PortsRate),
})
}
w.Flush()
}
func printPlotCommand(csvFile string) {
fmt.Println("\n=== 绘图命令 ===")
fmt.Println("\n# gnuplot:")
fmt.Printf(`gnuplot -e "
set terminal png size 800,600;
set output 'scalability.png';
set title 'fscan Scalability';
set xlabel 'Threads';
set ylabel 'Ports/sec';
set grid;
plot '%s' using 1:3 with linespoints title 'Throughput'
"
`, csvFile)
fmt.Println("\n# Python matplotlib:")
fmt.Println(`python -c "
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('` + csvFile + `')
plt.figure(figsize=(10,6))
plt.plot(df['threads'], df['ports_per_sec'], 'o-', linewidth=2, markersize=8)
plt.xlabel('Threads')
plt.ylabel('Ports/sec')
plt.title('fscan Scalability Chart')
plt.grid(True)
plt.savefig('scalability.png', dpi=150)
print('已保存: scalability.png')
"`)
}
tools/perftest/plot_benchmarks.py
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#!/usr/bin/env python3
"""
Go Benchmark 结果可视化脚本
生成柱状图展示各函数的性能指标
"""
import re
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
def parse_benchmark_results(filepath):
"""解析 Go benchmark 输出"""
results = []
with open(filepath, 'r', encoding='utf-8') as f:
for line in f:
# 匹配格式: BenchmarkXxx-24 123456 1.234 ns/op 123 B/op 12 allocs/op
match = re.match(
r'(Benchmark\w+)-\d+\s+(\d+)\s+([\d.]+)\s+(ns|µs|ms)/op(?:\s+([\d.]+)\s+B/op)?(?:\s+(\d+)\s+allocs/op)?',
line.strip()
)
if match:
name = match.group(1).replace('Benchmark', '')
ops = int(match.group(2))
time_val = float(match.group(3))
time_unit = match.group(4)
bytes_op = float(match.group(5)) if match.group(5) else 0
allocs_op = int(match.group(6)) if match.group(6) else 0
# 统一转换为 ns
if time_unit == 'µs':
time_ns = time_val * 1000
elif time_unit == 'ms':
time_ns = time_val * 1000000
else:
time_ns = time_val
results.append({
'name': name,
'ops': ops,
'time_ns': time_ns,
'time_val': time_val,
'time_unit': time_unit,
'bytes': bytes_op,
'allocs': allocs_op
})
return results
def format_time(ns):
"""格式化时间显示"""
if ns >= 1000000:
return f"{ns/1000000:.1f}ms"
elif ns >= 1000:
return f"{ns/1000:.1f}µs"
else:
return f"{ns:.1f}ns"
def format_bytes(b):
"""格式化内存显示"""
if b >= 1024*1024:
return f"{b/1024/1024:.1f}MB"
elif b >= 1024:
return f"{b/1024:.1f}KB"
else:
return f"{b:.0f}B"
def create_benchmark_charts(results, output_dir):
"""创建 benchmark 可视化图表"""
if not results:
print("没有找到 benchmark 结果")
return
# 按模块分组
core_funcs = [r for r in results if any(x in r['name'] for x in
['CheckSum', 'TCPDial', 'ResultCollector', 'FailedPort', 'Estimate', 'Calculate', 'BuildExclude', 'ArrayCount'])]
parser_funcs = [r for r in results if 'ParseIP' in r['name'] or 'ParsePort' in r['name']]
finger_funcs = [r for r in results if 'DecodePattern' in r['name']]
# 图1: 执行时间对比 (对数刻度)
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Go Benchmark 性能分析', fontsize=14, fontweight='bold')
# 子图1: 所有函数执行时间
ax1 = axes[0, 0]
names = [r['name'][:20] for r in results]
times = [r['time_ns'] for r in results]
colors = ['#2ecc71' if t < 1000 else '#f39c12' if t < 100000 else '#e74c3c' for t in times]
bars = ax1.barh(names, times, color=colors)
ax1.set_xscale('log')
ax1.set_xlabel('执行时间 (ns, 对数刻度)')
ax1.set_title('各函数执行时间')
# 添加数值标签
for bar, t in zip(bars, times):
ax1.text(t * 1.5, bar.get_y() + bar.get_height()/2,
format_time(t), va='center', fontsize=8)
# 子图2: 内存分配
ax2 = axes[0, 1]
mem_results = [r for r in results if r['bytes'] > 0]
if mem_results:
names = [r['name'][:20] for r in mem_results]
mem = [r['bytes'] for r in mem_results]
colors = ['#3498db' if m < 1024 else '#9b59b6' if m < 100000 else '#e74c3c' for m in mem]
bars = ax2.barh(names, mem, color=colors)
ax2.set_xscale('log')
ax2.set_xlabel('内存分配 (B, 对数刻度)')
ax2.set_title('各函数内存分配')
for bar, m in zip(bars, mem):
ax2.text(m * 1.5, bar.get_y() + bar.get_height()/2,
format_bytes(m), va='center', fontsize=8)
# 子图3: 核心模块详细对比
ax3 = axes[1, 0]
if core_funcs:
names = [r['name'][:18] for r in core_funcs]
times = [r['time_ns'] for r in core_funcs]
x = np.arange(len(names))
bars = ax3.bar(x, times, color='#3498db')
ax3.set_xticks(x)
ax3.set_xticklabels(names, rotation=45, ha='right', fontsize=8)
ax3.set_ylabel('执行时间 (ns)')
ax3.set_title('核心模块 (core) 性能')
ax3.set_yscale('log')
for bar, t in zip(bars, times):
ax3.text(bar.get_x() + bar.get_width()/2, t * 1.2,
format_time(t), ha='center', fontsize=7)
# 子图4: 解析器模块详细对比
ax4 = axes[1, 1]
if parser_funcs:
names = [r['name'].replace('ParseIP', 'IP').replace('ParsePort', 'Port')[:15] for r in parser_funcs]
times = [r['time_ns'] for r in parser_funcs]
x = np.arange(len(names))
bars = ax4.bar(x, times, color='#e74c3c')
ax4.set_xticks(x)
ax4.set_xticklabels(names, rotation=45, ha='right', fontsize=8)
ax4.set_ylabel('执行时间 (ns)')
ax4.set_title('解析器模块 (parsers) 性能')
ax4.set_yscale('log')
for bar, t in zip(bars, times):
ax4.text(bar.get_x() + bar.get_width()/2, t * 1.2,
format_time(t), ha='center', fontsize=7)
plt.tight_layout()
output_path = os.path.join(output_dir, 'benchmark_chart.png')
plt.savefig(output_path, dpi=150, bbox_inches='tight')
print(f"图表已保存: {output_path}")
plt.close()
# 图2: 性能热力图 - 时间 vs 内存
fig2, ax = plt.subplots(figsize=(10, 6))
# 筛选有内存分配的结果
valid_results = [r for r in results if r['bytes'] > 0]
if valid_results:
times = [r['time_ns'] for r in valid_results]
mems = [r['bytes'] for r in valid_results]
names = [r['name'][:15] for r in valid_results]
scatter = ax.scatter(times, mems, s=100, c=range(len(valid_results)),
cmap='viridis', alpha=0.7, edgecolors='black')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('执行时间 (ns)')
ax.set_ylabel('内存分配 (B)')
ax.set_title('性能-内存权衡分析')
# 添加标签
for i, (t, m, n) in enumerate(zip(times, mems, names)):
ax.annotate(n, (t, m), textcoords="offset points",
xytext=(5, 5), fontsize=7)
# 添加参考线
ax.axhline(y=1024, color='orange', linestyle='--', alpha=0.5, label='1KB')
ax.axhline(y=1024*1024, color='red', linestyle='--', alpha=0.5, label='1MB')
ax.axvline(x=1000, color='green', linestyle='--', alpha=0.5, label='1µs')
ax.axvline(x=1000000, color='purple', linestyle='--', alpha=0.5, label='1ms')
ax.legend(loc='upper left', fontsize=8)
output_path2 = os.path.join(output_dir, 'benchmark_tradeoff.png')
plt.savefig(output_path2, dpi=150, bbox_inches='tight')
print(f"图表已保存: {output_path2}")
plt.close()
def main():
if len(sys.argv) < 2:
# 默认路径
input_file = "results/benchmarks/benchmark_results.txt"
output_dir = "results/benchmarks"
else:
input_file = sys.argv[1]
output_dir = os.path.dirname(input_file) or "."
if not os.path.exists(input_file):
print(f"文件不存在: {input_file}")
sys.exit(1)
print(f"解析 benchmark 结果: {input_file}")
results = parse_benchmark_results(input_file)
print(f"找到 {len(results)} 个 benchmark 结果")
for r in results:
print(f" - {r['name']}: {format_time(r['time_ns'])}, {format_bytes(r['bytes'])}")
create_benchmark_charts(results, output_dir)
if __name__ == "__main__":
main()
tools/perftest/plot_internal_metrics.py
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#!/usr/bin/env python3
"""
fscan 内部指标可视化脚本
生成线程数-性能关系图
"""
import csv
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
def read_csv(filepath):
"""读取 CSV 结果文件"""
results = []
with open(filepath, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
results.append({
'threads': int(row['threads']),
'duration_ms': float(row['duration_ms']),
'pps': float(row['packets_per_sec']),
'total': int(row['total_packets']),
'success': int(row['tcp_success']),
'failed': int(row['tcp_failed']),
'success_rate': float(row['success_rate'])
})
return results
def create_charts(results, output_dir):
"""创建可视化图表"""
threads = [r['threads'] for r in results]
pps = [r['pps'] for r in results]
duration = [r['duration_ms']/1000 for r in results] # 转换为秒
# 创建 2x2 子图
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
fig.suptitle('fscan 内部指标性能分析 (目标: 1.1.1.0/24)', fontsize=14, fontweight='bold')
# 子图1: 吞吐量 vs 线程数
ax1 = axes[0, 0]
ax1.plot(threads, pps, 'o-', color='#2ecc71', linewidth=2, markersize=8, label='实测吞吐量')
# 找到最优点
max_pps_idx = np.argmax(pps)
ax1.axvline(x=threads[max_pps_idx], color='red', linestyle='--', alpha=0.7, label=f'最优线程数: {threads[max_pps_idx]}')
ax1.scatter([threads[max_pps_idx]], [pps[max_pps_idx]], color='red', s=150, zorder=5, marker='*')
ax1.set_xlabel('线程数')
ax1.set_ylabel('吞吐量 (packets/s)')
ax1.set_title('线程数 vs 吞吐量')
ax1.legend()
ax1.grid(True, alpha=0.3)
# 子图2: 扫描耗时 vs 线程数
ax2 = axes[0, 1]
ax2.plot(threads, duration, 's-', color='#e74c3c', linewidth=2, markersize=8)
ax2.set_xlabel('线程数')
ax2.set_ylabel('扫描耗时 (秒)')
ax2.set_title('线程数 vs 扫描耗时')
ax2.grid(True, alpha=0.3)
# 添加耗时标签
for t, d in zip(threads, duration):
ax2.annotate(f'{d:.1f}s', (t, d), textcoords="offset points",
xytext=(0, 10), ha='center', fontsize=8)
# 子图3: 效率分析 (吞吐量/线程数)
ax3 = axes[1, 0]
efficiency = [p/t*100 for p, t in zip(pps, threads)] # 每100线程的吞吐量
ax3.bar(range(len(threads)), efficiency, color='#3498db', alpha=0.7)
ax3.set_xticks(range(len(threads)))
ax3.set_xticklabels(threads)
ax3.set_xlabel('线程数')
ax3.set_ylabel('效率 (pps/100线程)')
ax3.set_title('线程效率分析')
# 添加数值标签
for i, e in enumerate(efficiency):
ax3.text(i, e + 0.5, f'{e:.1f}', ha='center', fontsize=9)
# 子图4: 加速比分析
ax4 = axes[1, 1]
base_pps = pps[0] # 200线程作为基准
speedup = [p/base_pps for p in pps]
ideal_speedup = [t/threads[0] for t in threads] # 理想线性加速
ax4.plot(threads, speedup, 'o-', color='#2ecc71', linewidth=2, markersize=8, label='实际加速比')
ax4.plot(threads, ideal_speedup, '--', color='#95a5a6', linewidth=1.5, label='理想线性加速')
ax4.set_xlabel('线程数')
ax4.set_ylabel('加速比 (相对于200线程)')
ax4.set_title('可扩展性分析')
ax4.legend()
ax4.grid(True, alpha=0.3)
plt.tight_layout()
output_path = os.path.join(output_dir, 'internal_metrics_chart.png')
plt.savefig(output_path, dpi=150, bbox_inches='tight')
print(f"图表已保存: {output_path}")
plt.close()
# 单独生成一张主要图表
fig2, ax = plt.subplots(figsize=(10, 6))
# 双Y轴
ax.set_xlabel('线程数', fontsize=12)
ax.set_ylabel('吞吐量 (packets/s)', color='#2ecc71', fontsize=12)
line1 = ax.plot(threads, pps, 'o-', color='#2ecc71', linewidth=2.5, markersize=10, label='吞吐量')
ax.tick_params(axis='y', labelcolor='#2ecc71')
ax.axvline(x=threads[max_pps_idx], color='red', linestyle='--', alpha=0.5)
ax.scatter([threads[max_pps_idx]], [pps[max_pps_idx]], color='red', s=200, zorder=5, marker='*')
ax2 = ax.twinx()
ax2.set_ylabel('扫描耗时 (秒)', color='#e74c3c', fontsize=12)
line2 = ax2.plot(threads, duration, 's--', color='#e74c3c', linewidth=2, markersize=8, label='耗时')
ax2.tick_params(axis='y', labelcolor='#e74c3c')
# 合并图例
lines = line1 + line2
labels = [l.get_label() for l in lines]
ax.legend(lines, labels, loc='center right')
ax.set_title('fscan 内部指标: 线程数 vs 性能\n(目标: 1.1.1.0/24, 端口: 22,80,443,3389,8080)', fontsize=13)
ax.grid(True, alpha=0.3)
# 添加最优点标注
ax.annotate(f'最优: {threads[max_pps_idx]}线程\n{pps[max_pps_idx]:.1f} pps',
xy=(threads[max_pps_idx], pps[max_pps_idx]),
xytext=(threads[max_pps_idx]+200, pps[max_pps_idx]-10),
arrowprops=dict(arrowstyle='->', color='red'),
fontsize=10, color='red')
output_path2 = os.path.join(output_dir, 'scalability_chart.png')
plt.savefig(output_path2, dpi=150, bbox_inches='tight')
print(f"图表已保存: {output_path2}")
plt.close()
def main():
if len(sys.argv) < 2:
input_file = "results/internal_metrics/precise_results.csv"
output_dir = "results/internal_metrics"
else:
input_file = sys.argv[1]
output_dir = os.path.dirname(input_file) or "."
if not os.path.exists(input_file):
print(f"文件不存在: {input_file}")
sys.exit(1)
print(f"读取数据: {input_file}")
results = read_csv(input_file)
print(f"找到 {len(results)} 条记录")
for r in results:
print(f" 线程={r['threads']}: {r['pps']:.1f} pps, 耗时={r['duration_ms']/1000:.1f}s")
create_charts(results, output_dir)
if __name__ == "__main__":
main()
tools/perftest/plot_param_tests.py
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#!/usr/bin/env python3
"""
fscan 参数测试结果可视化
"""
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
def read_csv(filepath):
results = []
with open(filepath, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
results.append(row)
return results
def create_charts(output_dir):
# 读取数据
time_file = os.path.join(output_dir, "time_results.csv")
mt_file = os.path.join(output_dir, "mt_results.csv")
time_data = read_csv(time_file)
mt_data = read_csv(mt_file)
# 创建图表
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
fig.suptitle('fscan 参数性能测试 (目标: 1.1.1.0/24)', fontsize=14, fontweight='bold')
# ==================== 图1: -time 超时参数 ====================
ax1 = axes[0]
times = [int(d['time_seconds']) for d in time_data]
pps = [float(d['packets_per_sec']) for d in time_data]
duration = [float(d['duration_ms'])/1000 for d in time_data]
# 双Y轴
color1 = '#2ecc71'
ax1.set_xlabel('超时时间 -time (秒)', fontsize=11)
ax1.set_ylabel('吞吐量 (pps)', color=color1, fontsize=11)
bars = ax1.bar([x - 0.2 for x in range(len(times))], pps, 0.4, color=color1, alpha=0.7, label='吞吐量')
ax1.tick_params(axis='y', labelcolor=color1)
ax1.set_xticks(range(len(times)))
ax1.set_xticklabels([f'{t}s' for t in times])
# 标注最优值
max_idx = np.argmax(pps)
ax1.bar(max_idx - 0.2, pps[max_idx], 0.4, color='#27ae60', alpha=0.9, edgecolor='red', linewidth=2)
ax1_twin = ax1.twinx()
color2 = '#e74c3c'
ax1_twin.set_ylabel('扫描耗时 (秒)', color=color2, fontsize=11)
ax1_twin.bar([x + 0.2 for x in range(len(times))], duration, 0.4, color=color2, alpha=0.7, label='耗时')
ax1_twin.tick_params(axis='y', labelcolor=color2)
# 添加数值标签
for i, (p, d) in enumerate(zip(pps, duration)):
ax1.text(i - 0.2, p + 2, f'{p:.1f}', ha='center', fontsize=9, color=color1)
ax1_twin.text(i + 0.2, d + 0.3, f'{d:.1f}s', ha='center', fontsize=9, color=color2)
ax1.set_title('-time 超时参数影响\n(默认值: 3秒)', fontsize=12)
ax1.axhline(y=pps[2], color='gray', linestyle='--', alpha=0.5, label='默认值基准')
# 计算相对于默认值的提升
default_pps = pps[2] # time=3 是默认值
improvement = [(p - default_pps) / default_pps * 100 for p in pps]
# ==================== 图2: -mt 模块线程参数 ====================
ax2 = axes[1]
mts = [int(d['module_threads']) for d in mt_data]
mt_pps = [float(d['packets_per_sec']) for d in mt_data]
mt_duration = [float(d['duration_ms'])/1000 for d in mt_data]
ax2.bar(range(len(mts)), mt_pps, color='#3498db', alpha=0.7)
ax2.set_xlabel('模块线程数 -mt', fontsize=11)
ax2.set_ylabel('吞吐量 (pps)', fontsize=11)
ax2.set_xticks(range(len(mts)))
ax2.set_xticklabels(mts)
ax2.set_title('-mt 模块线程参数影响\n(默认值: 20, 测试时禁用POC)', fontsize=12)
# 添加数值标签
for i, p in enumerate(mt_pps):
ax2.text(i, p + 0.5, f'{p:.1f}', ha='center', fontsize=9)
# 计算变化范围
mt_range = max(mt_pps) - min(mt_pps)
ax2.set_ylim(min(mt_pps) - 5, max(mt_pps) + 5)
# 添加注释
ax2.text(0.5, 0.95, f'变化幅度: {mt_range:.2f} pps (可忽略)',
transform=ax2.transAxes, ha='center', fontsize=10,
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
plt.tight_layout()
output_path = os.path.join(output_dir, 'param_test_chart.png')
plt.savefig(output_path, dpi=150, bbox_inches='tight')
print(f"图表已保存: {output_path}")
plt.close()
# ==================== 生成详细分析图 ====================
fig2, ax = plt.subplots(figsize=(10, 6))
x = np.arange(len(times))
width = 0.35
# 性能提升百分比
colors = ['#e74c3c' if imp < 0 else '#2ecc71' for imp in improvement]
bars = ax.bar(x, improvement, width, color=colors, alpha=0.8)
ax.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
ax.set_xlabel('超时时间 -time (秒)', fontsize=12)
ax.set_ylabel('相对默认值(3秒)的性能变化 (%)', fontsize=12)
ax.set_title('-time 参数优化效果分析', fontsize=14, fontweight='bold')
ax.set_xticks(x)
ax.set_xticklabels([f'{t}s' for t in times])
# 添加数值标签
for i, (bar, imp) in enumerate(zip(bars, improvement)):
height = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2., height + (1 if height >= 0 else -3),
f'{imp:+.1f}%', ha='center', va='bottom' if height >= 0 else 'top',
fontsize=11, fontweight='bold')
# 添加建议
ax.text(0.02, 0.98,
'建议:\n• 内网环境: -time 1 或 2\n• 公网环境: -time 3 (默认)\n• 高延迟网络: -time 5+',
transform=ax.transAxes, fontsize=10, verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='lightyellow', alpha=0.8))
plt.tight_layout()
output_path2 = os.path.join(output_dir, 'time_optimization_chart.png')
plt.savefig(output_path2, dpi=150, bbox_inches='tight')
print(f"图表已保存: {output_path2}")
plt.close()
def main():
output_dir = "results/param_tests"
create_charts(output_dir)
if __name__ == "__main__":
main()
tools/perftest/plot_results.py
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#!/usr/bin/env python3
"""
fscan 可扩展性图表生成工具
用法:
python plot_results.py perf_results.csv
python plot_results.py perf_results.csv -o my_chart.png
"""
import sys
import argparse
def main():
parser = argparse.ArgumentParser(description='绘制 fscan 可扩展性图表')
parser.add_argument('csv_file', help='CSV 数据文件')
parser.add_argument('-o', '--output', default='scalability.png', help='输出图片文件')
parser.add_argument('--style', choices=['default', 'dark', 'minimal'], default='default')
args = parser.parse_args()
try:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib as mpl
except ImportError:
print("需要安装依赖: pip install pandas matplotlib")
sys.exit(1)
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
# 读取数据
df = pd.read_csv(args.csv_file)
# 创建图表
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
# 样式设置
if args.style == 'dark':
plt.style.use('dark_background')
color1, color2 = '#00ff88', '#ff6b6b'
else:
color1, color2 = '#2563eb', '#dc2626'
# 图1: 吞吐量 vs 线程数
ax1.plot(df['threads'], df['ports_per_sec'], 'o-',
color=color1, linewidth=2.5, markersize=10, label='实测吞吐量')
# 理想线性扩展线（以第一个点为基准）
if len(df) > 0:
base_rate = df['ports_per_sec'].iloc[0]
base_threads = df['threads'].iloc[0]
ideal = [base_rate * (t / base_threads) for t in df['threads']]
ax1.plot(df['threads'], ideal, '--', color='gray', alpha=0.5, label='理想线性扩展')
ax1.set_xlabel('线程数', fontsize=12)
ax1.set_ylabel('扫描速率 (端口/秒)', fontsize=12)
ax1.set_title('fscan 可扩展性曲线', fontsize=14, fontweight='bold')
ax1.grid(True, alpha=0.3)
ax1.legend(loc='upper left')
# 标注峰值点
max_idx = df['ports_per_sec'].idxmax()
max_threads = df['threads'].iloc[max_idx]
max_rate = df['ports_per_sec'].iloc[max_idx]
ax1.annotate(f'峰值: {max_rate:.0f} 端口/秒\n@ {max_threads} 线程',
xy=(max_threads, max_rate),
xytext=(max_threads - 200, max_rate * 0.75),
fontsize=10,
arrowprops=dict(arrowstyle='->', color='gray'))
# 图2: 扫描耗时 vs 线程数
ax2.plot(df['threads'], df['duration_sec'], 's-',
color=color2, linewidth=2.5, markersize=10)
ax2.set_xlabel('线程数', fontsize=12)
ax2.set_ylabel('扫描耗时 (秒)', fontsize=12)
ax2.set_title('扫描耗时曲线', fontsize=14, fontweight='bold')
ax2.grid(True, alpha=0.3)
# 标注最快点
min_idx = df['duration_sec'].idxmin()
min_threads = df['threads'].iloc[min_idx]
min_duration = df['duration_sec'].iloc[min_idx]
ax2.annotate(f'最快: {min_duration:.2f} 秒\n@ {min_threads} 线程',
xy=(min_threads, min_duration),
xytext=(min_threads - 200, min_duration * 1.5),
fontsize=10,
arrowprops=dict(arrowstyle='->', color='gray'))
plt.tight_layout()
plt.savefig(args.output, dpi=150, bbox_inches='tight')
print(f"图表已保存: {args.output}")
# 打印分析结论
print("\n=== 分析结论 ===")
print(f"最优线程数: {max_threads} (峰值吞吐量: {max_rate:.0f} ports/sec)")
print(f"最短耗时: {min_duration:.2f}s @ {min_threads} 线程")
# 计算扩展效率
if len(df) >= 2:
efficiency = (df['ports_per_sec'].iloc[-1] / df['ports_per_sec'].iloc[0]) / \
(df['threads'].iloc[-1] / df['threads'].iloc[0]) * 100
print(f"扩展效率: {efficiency:.1f}% (相对于线性扩展)")
if efficiency < 50:
print("⚠️ 扩展效率较低，可能存在锁竞争或资源瓶颈")
elif efficiency > 80:
print("✅ 扩展效率良好")
if __name__ == '__main__':
main()
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tools/perftest/results/benchmarks/benchmark_results.txt
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BenchmarkCheckSum-24 659448997 1.821 ns/op 0 B/op 0 allocs/op
BenchmarkArrayCountValueTop-24 4047 257006 ns/op 329822 B/op 5040 allocs/op
BenchmarkTCPDial-24 4596 473517 ns/op 2424 B/op 27 allocs/op
BenchmarkResultCollectorAdd-24 31689693 42.06 ns/op 99 B/op 0 allocs/op
BenchmarkResultCollectorAddParallel-24 16319025 73.03 ns/op 99 B/op 0 allocs/op
BenchmarkResultCollectorGetAll-24 501963 2320 ns/op 16384 B/op 1 allocs/op
BenchmarkFailedPortCollectorAdd-24 18020209 72.26 ns/op 218 B/op 0 allocs/op
BenchmarkEstimateScanTime-24 1000000000 0.1474 ns/op 0 B/op 0 allocs/op
BenchmarkCalculateTotalTasks-24 345832 3346 ns/op 0 B/op 0 allocs/op
BenchmarkBuildExcludeMap-24 8549785 143.4 ns/op 328 B/op 3 allocs/op
ok github.com/shadow1ng/fscan/core 14.384s
BenchmarkDecodePattern-24 23028650 44.72 ns/op 56 B/op 3 allocs/op
BenchmarkDecodePattern_Complex-24 8355910 144.3 ns/op 40 B/op 13 allocs/op
ok github.com/shadow1ng/fscan/core/portfinger 3.008s
BenchmarkParseIPCIDR24-24 40768 29300 ns/op 57881 B/op 294 allocs/op
BenchmarkParseIPCIDR16-24 829 1394559 ns/op 2692984 B/op 10122 allocs/op
BenchmarkParseIPRange-24 42494 28765 ns/op 57816 B/op 289 allocs/op
BenchmarkParseIPSingle-24 15086103 76.51 ns/op 64 B/op 4 allocs/op
BenchmarkParsePortRange-24 303 4050534 ns/op 10277983 B/op 585 allocs/op
BenchmarkParsePortList-24 1667944 700.6 ns/op 968 B/op 14 allocs/op
BenchmarkParsePortCommon-24 1007536 1237 ns/op 1672 B/op 16 allocs/op
ok github.com/shadow1ng/fscan/common/parsers 11.298s
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tools/perftest/results/cloudflare_anycast/perf_results.csv
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"threads","duration_sec","ports_per_sec","total_ports"
"200","30.221","42","1270"
"400","19.124","66.4","1270"
"600","16.162","78.6","1270"
"800","14.099","90.1","1270"
"1000","12.127","104.7","1270"
"1500","12.102","104.9","1270"
"2000","12.114","104.8","1270"
"3000","12.122","104.8","1270"
"4000","12.074","105.2","1270"
"5000","12.116","104.8","1270"
1 threads duration_sec ports_per_sec total_ports
2 200 30.221 42 1270
3 400 19.124 66.4 1270
4 600 16.162 78.6 1270
5 800 14.099 90.1 1270
6 1000 12.127 104.7 1270
7 1500 12.102 104.9 1270
8 2000 12.114 104.8 1270
9 3000 12.122 104.8 1270
10 4000 12.074 105.2 1270
11 5000 12.116 104.8 1270
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tools/perftest/results/internal_metrics/precise_results.csv
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"threads","duration_ms","packets_per_sec","total_packets","tcp_success","tcp_failed","success_rate"
200,29330,43.3,1270,759,511,59.8
400,18730,67.8,1270,759,511,59.8
600,15270,83.2,1270,759,511,59.8
800,13090,97.2,1270,759,511,59.8
1000,11660,109.0,1270,759,511,59.8
1500,11500,110.9,1270,759,511,59.8
2000,11900,106.8,1270,759,511,59.8
1 threads duration_ms packets_per_sec total_packets tcp_success tcp_failed success_rate
2 200 29330 43.3 1270 759 511 59.8
3 400 18730 67.8 1270 759 511 59.8
4 600 15270 83.2 1270 759 511 59.8
5 800 13090 97.2 1270 759 511 59.8
6 1000 11660 109.0 1270 759 511 59.8
7 1500 11500 110.9 1270 759 511 59.8
8 2000 11900 106.8 1270 759 511 59.8
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tools/perftest/results/param_tests/mt_results.csv
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"module_threads","duration_ms","packets_per_sec","tcp_success","tcp_failed","success_rate"
"5","15211","83.49","759","511","59.76"
"10","15253","83.26","759","511","59.76"
"20","15221","83.44","759","511","59.76"
"50","15254","83.26","759","511","59.76"
"100","15217","83.46","759","511","59.76"
1 module_threads duration_ms packets_per_sec tcp_success tcp_failed success_rate
2 5 15211 83.49 759 511 59.76
3 10 15253 83.26 759 511 59.76
4 20 15221 83.44 759 511 59.76
5 50 15254 83.26 759 511 59.76
6 100 15217 83.46 759 511 59.76
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tools/perftest/results/param_tests/time_results.csv
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"time_seconds","duration_ms","packets_per_sec","tcp_success","tcp_failed","success_rate"
"1","13110","96.87","759","511","59.76"
"2","13295","95.52","759","511","59.76"
"3","15231","83.38","759","511","59.76"
"5","16451","77.20","759","511","59.76"
1 time_seconds duration_ms packets_per_sec tcp_success tcp_failed success_rate
2 1 13110 96.87 759 511 59.76
3 2 13295 95.52 759 511 59.76
4 3 15231 83.38 759 511 59.76
5 5 16451 77.20 759 511 59.76
tools/perftest/run_param_test.ps1
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# fscan 参数性能测试脚本
# 测试 -time 和 -mt 参数对性能的影响
param(
[Parameter(Mandatory=$true)]
[string]$Target,
[string]$Ports = "22,80,443,3389,8080",
[int]$Threads = 600,
[string]$OutputDir = "results/param_tests"
)
$fscanPath = Join-Path $PSScriptRoot "..\..\fscan.exe"
if (-not (Test-Path $fscanPath)) {
$fscanPath = "fscan.exe"
}
# 创建输出目录
$fullOutputDir = Join-Path $PSScriptRoot $OutputDir
New-Item -ItemType Directory -Force -Path $fullOutputDir | Out-Null
Write-Host "=== fscan 参数性能测试 ===" -ForegroundColor Cyan
Write-Host "目标: $Target"
Write-Host "端口: $Ports"
Write-Host "基准线程: $Threads"
Write-Host ""
# ==================== 测试1: -time 超时参数 ====================
Write-Host ">>> 测试1: -time 超时参数 <<<" -ForegroundColor Yellow
$timeValues = @(1, 2, 3, 5)
$timeResults = @()
foreach ($t in $timeValues) {
Write-Host "[time=$t] " -NoNewline
$output = & $fscanPath -h $Target -p $Ports -t $Threads -time $t -np -nopoc -perf -no 2>&1 | Out-String
if ($output -match '\[PERF_STATS_JSON\](.*?)\[/PERF_STATS_JSON\]') {
$stats = $Matches[1] | ConvertFrom-Json
$timeResults += [PSCustomObject]@{
time_seconds = $t
duration_ms = $stats.scan_duration_ms
packets_per_sec = [math]::Round($stats.packets_per_second, 2)
tcp_success = $stats.tcp_success
tcp_failed = $stats.tcp_failed
success_rate = [math]::Round($stats.success_rate, 2)
}
Write-Host "耗时: $([math]::Round($stats.scan_duration_ms/1000, 2))s, $([math]::Round($stats.packets_per_second, 1)) pps, 成功率: $([math]::Round($stats.success_rate, 1))%" -ForegroundColor Green
} else {
Write-Host "解析失败" -ForegroundColor Red
}
}
$timeResults | Export-Csv -Path (Join-Path $fullOutputDir "time_results.csv") -NoTypeInformation
Write-Host ""
# ==================== 测试2: -mt 模块线程参数 ====================
Write-Host ">>> 测试2: -mt 模块线程参数 <<<" -ForegroundColor Yellow
$mtValues = @(5, 10, 20, 50, 100)
$mtResults = @()
foreach ($mt in $mtValues) {
Write-Host "[mt=$mt] " -NoNewline
# 注意: -mt 主要影响服务识别和POC，这里不用 -nopoc 来观察效果
$output = & $fscanPath -h $Target -p $Ports -t $Threads -mt $mt -np -nopoc -perf -no 2>&1 | Out-String
if ($output -match '\[PERF_STATS_JSON\](.*?)\[/PERF_STATS_JSON\]') {
$stats = $Matches[1] | ConvertFrom-Json
$mtResults += [PSCustomObject]@{
module_threads = $mt
duration_ms = $stats.scan_duration_ms
packets_per_sec = [math]::Round($stats.packets_per_second, 2)
tcp_success = $stats.tcp_success
tcp_failed = $stats.tcp_failed
success_rate = [math]::Round($stats.success_rate, 2)
}
Write-Host "耗时: $([math]::Round($stats.scan_duration_ms/1000, 2))s, $([math]::Round($stats.packets_per_second, 1)) pps" -ForegroundColor Green
} else {
Write-Host "解析失败" -ForegroundColor Red
}
}
$mtResults | Export-Csv -Path (Join-Path $fullOutputDir "mt_results.csv") -NoTypeInformation
Write-Host ""
Write-Host "=== 测试完成 ===" -ForegroundColor Cyan
Write-Host "结果保存到: $fullOutputDir" -ForegroundColor Yellow
# 打印汇总
Write-Host ""
Write-Host ">>> -time 测试结果 <<<" -ForegroundColor Cyan
$timeResults | Format-Table -AutoSize
Write-Host ">>> -mt 测试结果 <<<" -ForegroundColor Cyan
$mtResults | Format-Table -AutoSize
tools/perftest/run_perftest.bat
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@echo off
setlocal enabledelayedexpansion
:: fscan 可扩展性测试脚本
:: 用法: run_perftest.bat 192.168.1.0/24
set TARGET=%1
if "%TARGET%"=="" (
echo 用法: run_perftest.bat ^<target^>
echo 示例: run_perftest.bat 192.168.1.0/24
exit /b 1
)
set PORTS=22,80,443,3389,8080
set THREADS=100 200 400 600 800 1000 1500 2000
set OUTPUT=perf_results.csv
echo threads,duration_sec,timestamp > %OUTPUT%
echo === fscan 可扩展性测试 ===
echo 目标: %TARGET%
echo 端口: %PORTS%
for %%t in (%THREADS%) do (
echo.
echo [测试] 线程数=%%t
:: 记录开始时间
set START=%time%
:: 运行 fscan
fscan.exe -h %TARGET% -p %PORTS% -t %%t -np -nopoc -o NUL 2>NUL
:: 记录结束时间并计算耗时
set END=%time%
:: 简单输出（实际耗时需要手动计算或用 PowerShell）
echo %%t,%START%-%END%,%date% >> %OUTPUT%
echo 完成: %%t 线程
)
echo.
echo 结果已保存到: %OUTPUT%
echo 使用 plot_results.py 绘图
tools/perftest/run_perftest.ps1
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# fscan 可扩展性测试 PowerShell 脚本
# 用法: .\run_perftest.ps1 -Target 192.168.1.0/24
param(
[Parameter(Mandatory=$true)]
[string]$Target,
[string]$Ports = "22,80,443,3389,8080",
[int[]]$Threads = @(100, 200, 400, 600, 800, 1000, 1500),
[int]$Repeat = 3,
[string]$Output = "perf_results.csv"
)
$fscanPath = Join-Path $PSScriptRoot "..\..\fscan.exe"
if (-not (Test-Path $fscanPath)) {
$fscanPath = "fscan.exe"
}
Write-Host "=== fscan 可扩展性测试 ===" -ForegroundColor Cyan
Write-Host "目标: $Target"
Write-Host "端口: $Ports"
Write-Host "线程数: $($Threads -join ', ')"
Write-Host "重复次数: $Repeat"
Write-Host ""
$results = @()
foreach ($t in $Threads) {
Write-Host "[线程=$t] " -NoNewline
$durations = @()
for ($i = 1; $i -le $Repeat; $i++) {
Write-Host "." -NoNewline
$stopwatch = [System.Diagnostics.Stopwatch]::StartNew()
$proc = Start-Process -FilePath $fscanPath -ArgumentList @(
"-h", $Target,
"-p", $Ports,
"-t", $t,
"-np",
"-nopoc",
"-o", "NUL"
) -NoNewWindow -Wait -PassThru
$stopwatch.Stop()
$durations += $stopwatch.Elapsed.TotalSeconds
}
$avgDuration = ($durations | Measure-Object -Average).Average
# 估算端口扫描数
$portCount = ($Ports -split ',').Count
if ($Target -match '/24') { $ipCount = 254 }
elseif ($Target -match '/16') { $ipCount = 65534 }
else { $ipCount = 1 }
$totalPorts = $ipCount * $portCount
$portsPerSec = if ($avgDuration -gt 0) { $totalPorts / $avgDuration } else { 0 }
$results += [PSCustomObject]@{
threads = $t
duration_sec = [math]::Round($avgDuration, 3)
ports_per_sec = [math]::Round($portsPerSec, 1)
total_ports = $totalPorts
}
Write-Host " 平均: $([math]::Round($avgDuration, 2))s, $([math]::Round($portsPerSec, 0)) ports/sec" -ForegroundColor Green
}
# 导出 CSV
$results | Export-Csv -Path $Output -NoTypeInformation
Write-Host "`n结果已保存到: $Output" -ForegroundColor Yellow
# 打印绘图命令
Write-Host "`n=== 绘图命令 ===" -ForegroundColor Cyan
Write-Host "python plot_results.py $Output"
tools/perftest/run_precise_test.ps1
+77
View File
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# fscan 精确性能测试脚本 (使用内部指标)
# 用法: .\run_precise_test.ps1 -Target 1.1.1.0/24
param(
[Parameter(Mandatory=$true)]
[string]$Target,
[string]$Ports = "22,80,443,3389,8080",
[int[]]$Threads = @(200, 400, 600, 800, 1000, 1500, 2000),
[int]$Repeat = 1,
[string]$Output = "precise_results.csv"
)
$fscanPath = Join-Path $PSScriptRoot "..\..\fscan.exe"
if (-not (Test-Path $fscanPath)) {
$fscanPath = "fscan.exe"
}
Write-Host "=== fscan 精确性能测试 (内部指标) ===" -ForegroundColor Cyan
Write-Host "目标: $Target"
Write-Host "端口: $Ports"
Write-Host "线程数: $($Threads -join ', ')"
Write-Host ""
$results = @()
foreach ($t in $Threads) {
Write-Host "[线程=$t] " -NoNewline
$allStats = @()
for ($i = 1; $i -le $Repeat; $i++) {
Write-Host "." -NoNewline
# 运行 fscan 并捕获输出
$output = & $fscanPath -h $Target -p $Ports -t $t -np -nopoc -perf -no 2>&1 | Out-String
# 提取 JSON
if ($output -match '\[PERF_STATS_JSON\](.*?)\[/PERF_STATS_JSON\]') {
$jsonStr = $Matches[1]
$stats = $jsonStr | ConvertFrom-Json
$allStats += $stats
}
}
if ($allStats.Count -gt 0) {
# 计算平均值
$avgDuration = ($allStats | Measure-Object -Property scan_duration_ms -Average).Average
$avgPPS = ($allStats | Measure-Object -Property packets_per_second -Average).Average
$avgTotal = ($allStats | Measure-Object -Property total_packets -Average).Average
$avgSuccess = ($allStats | Measure-Object -Property tcp_success -Average).Average
$avgFailed = ($allStats | Measure-Object -Property tcp_failed -Average).Average
$avgSuccessRate = ($allStats | Measure-Object -Property success_rate -Average).Average
$results += [PSCustomObject]@{
threads = $t
duration_ms = [math]::Round($avgDuration, 0)
packets_per_sec = [math]::Round($avgPPS, 2)
total_packets = [math]::Round($avgTotal, 0)
tcp_success = [math]::Round($avgSuccess, 0)
tcp_failed = [math]::Round($avgFailed, 0)
success_rate = [math]::Round($avgSuccessRate, 2)
}
Write-Host " 耗时: $([math]::Round($avgDuration/1000, 2))s, $([math]::Round($avgPPS, 1)) pkt/s, 成功率: $([math]::Round($avgSuccessRate, 1))%" -ForegroundColor Green
} else {
Write-Host " 解析失败" -ForegroundColor Red
}
}
# 导出 CSV
$results | Export-Csv -Path $Output -NoTypeInformation
Write-Host "`n结果已保存到: $Output" -ForegroundColor Yellow
# 打印数据表格
Write-Host "`n=== 测试结果 ===" -ForegroundColor Cyan
$results | Format-Table -AutoSize