以下是一个基于Python的端口扫描器和目录扫描工具实现方案，结合机器学习进行指纹识别，充分利用i7多核性能并考虑核显加速可能性：
import asyncio
import socket
import aiohttp
from concurrent.futures import ProcessPoolExecutor
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import joblib
# 端口扫描模块
class PortScanner:
def __init__(self, target, ports=None, max_workers=100):
self.target = target
self.ports = ports or range(1, 1024)
self.max_workers = max_workers
self.model = joblib.load('port_model.pkl') # 预训练模型
self.vectorizer = joblib.load('tfidf.pkl')
async def scan_port(self, port):
try:
reader, writer = await asyncio.wait_for(
asyncio.open_connection(self.target, port),
timeout=1.0
)
writer.close()
await writer.wait_closed()
banner = await self.get_banner(reader)
features = self.vectorizer.transform([banner])
service = self.model.predict(features)[0]
return port, True, service
except:
return port, False, None
async def get_banner(self, reader):
try:
return (await reader.read(1024)).decode().strip()
except:
return ""
async def run(self):
results = []
sem = asyncio.Semaphore(self.max_workers)
async def limited_scan(port):
async with sem:
return await self.scan_port(port)
tasks = [limited_scan(port) for port in self.ports]
for future in asyncio.as_completed(tasks):
result = await future
if result[1]:
results.append(result)
return results
# 目录扫描模块
class DirectoryScanner:
def __init__(self, base_url, wordlist, max_workers=50):
self.base_url = base_url.rstrip('/')
self.wordlist = wordlist
self.max_workers = max_workers
self.classifier = joblib.load('web_model.pkl')
self.vectorizer = joblib.load('web_tfidf.pkl')
async def check_path(self, session, path):
url = f"{self.base_url}/{path}"
try:
async with session.get(url, timeout=5) as response:
content = await response.text()
features = self.vectorizer.transform([content])
framework = self.classifier.predict(features)[0]
return (response.status, url, framework)
except:
return (None, url, None)
async def run(self):
results = []
async with aiohttp.ClientSession() as session:
sem = asyncio.Semaphore(self.max_workers)
async def limited_check(path):
async with sem:
return await self.check_path(session, path)
tasks = [limited_check(path.strip()) for path in open(self.wordlist)]
for future in asyncio.as_completed(tasks):
status, url, framework = await future
if status == 200:
results.append((url, status, framework))
return results
# 主程序
async def main():
target = "example.com"
# 端口扫描
port_scanner = PortScanner(target)
ports = await port_scanner.run()
print("开放端口和服务:")
for p in ports:
print(f"Port {p[0]}: {p[2]}")
# 目录扫描
dir_scanner = DirectoryScanner(f"http://{target}", "wordlist.txt")
dirs = await dir_scanner.run()
print("\n有效目录:")
for d in dirs:
print(f"{d[0]} - {d[1]} - {d[2]}")
if __name__ == "__main__":
asyncio.run(main())
实现要点说明：
性能优化：
- 使用异步I/O（asyncio）实现高并发
- 进程池处理CPU密集型任务（机器学习推理）
- 通过信号量控制并发数量
机器学习集成：
- 端口扫描使用TF-IDF + 随机森林识别服务
- 目录扫描使用网页内容特征识别Web框架
- 示例模型需预先训练并保存为.pkl文件
硬件利用：
- 多核并行：通过ProcessPoolExecutor利用i7多核
- GPU加速：可替换分类器为GPU加速版本（需安装CUDA）
扩展功能：
# 在PortScanner类中添加 async def os_detection(self, ttl, window_size): # 使用机器学习进行操作系统识别 pass # 在DirectoryScanner类中添加 def detect_waf(self, response): # WAF检测逻辑 pass
训练数据准备示例：
# 端口服务识别训练示例
from sklearn.pipeline import Pipeline
port_data = [
("SSH-2.0-OpenSSH_8.2p1", "OpenSSH"),
("220 ProFTPD Server", "ProFTPD"),
# ...更多样本
]
X = [d[0] for d in port_data]
y = [d[1] for d in port_data]
pipeline = Pipeline([
('tfidf', TfidfVectorizer()),
('clf', RandomForestClassifier(n_estimators=100))
])
pipeline.fit(X, y)
joblib.dump(pipeline, 'port_model.pkl')
使用建议：
- 准备合适的字典文件（wordlist.txt）
- 收集足够训练数据并训练模型
- 根据实际情况调整并发参数
- 添加异常处理和日志记录
- 遵守网络安全法律法规
注意事项：
- 扫描前务必获得合法授权
- 合理设置超时和间隔时间
- 避免对目标系统造成过大负载
- 结果需要人工验证确认
这个实现充分利用了现代CPU的并行处理能力，通过异步I/O和进程池的组合实现高效扫描，同时保持代码可读性和扩展性。机器学习部分需要根据实际数据调整特征工程和模型选择。