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出口企业网站建设,不通过第三方平台做微网站,哪个网站做外贸好,企业网站那几点重要以下是一个基于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 sk…

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 = targetself.ports = ports or range(1, 1024)self.max_workers = max_workersself.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, serviceexcept:return port, False, Noneasync 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 futureif 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 = wordlistself.max_workers = max_workersself.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 futureif 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 Pipelineport_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')