Dynamic proxy ip detection tool, dynamic proxy usage scenarios

Low Frequency Crawler Protection with Dynamic Proxy Second IP Dialing
As a network security engineer, I often have to deal with a variety of network attacks and crawling behavior. Among them, low-frequency crawlers with dynamic proxies dialing IPs in seconds is one of the challenges we often encounter. In this post, I will share some experiences and tips on how to prevent such attacks.

Hazards of low-frequency creepy crawlies
First, let's understand the dangers of low-frequency crawlers. Low-frequency crawlers usually use dynamic proxies and second-dial IPs to mimic human behavior in order to circumvent a website's anti-crawler strategy. They generally do not visit websites frequently, but crawl at a lower frequency so as not to be recognized by the website. Although this kind of crawler behavior does not cause large-scale network congestion and data leakage, it poses a certain threat to the normal operation of the website and data security.

Analyzing the behavioral patterns of low-frequency crawlers
To effectively prevent low-frequency crawlers, we first need to deeply analyze their behavioral patterns. We can monitor website access logs and traffic data to analyze the frequency of IP access and access time interval. In addition, we can also use some network traffic analysis tools, such as Wireshark, to capture and analyze the network request packets of low-frequency crawlers. Through these analyses, we can better understand the behavioral patterns of low-frequency crawlers, so as to develop targeted prevention strategies.

Utilizing Dynamic IP Proxy Recognition Technology
For the characteristics of low-frequency crawlers that utilize dynamic proxies and second-dial IPs, we can use some IP proxy identification techniques to prevent them. One common method is to utilize the black and white IP list mechanism, i.e., adding known dynamic proxies and second-dial IP addresses to the blacklist and denying their access requests. At the same time, we can also build our own IP proxy pool, regularly update high-quality proxy IP addresses, and use programs to automatically identify and filter malicious dynamic proxies and second-dial IPs.

Sample code:

import requestsdef check_proxy(proxy).
url = 'http://www.example.com' # change to specific website address
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3',
}
try.
response = requests.get(url, headers=headers, proxies={'http': proxy, 'https': proxy}, timeout=3)
if response.status_code == 200: if response.status_code == 200: if response.status_code == 200
return True
return True: if response.status_code == 200: return True
return False
except: return False
return False

def get_valid_proxy():
proxy_pool = ['http://1.1.1.1:8888', 'http://2.2.2.2:8888', 'http://3.3.3.3:8888'] # Change to your own pool of proxy IP addresses.
valid_proxy_pool = []
for proxy in proxy_pool.
if check_proxy(proxy).
valid_proxy_pool.append(proxy)
else: valid_proxy_pool.append(proxy)
valid_proxy_pool.append(proxy else)
return valid_proxy_pool

Limit the frequency of visits by low-frequency crawlers
In addition to identifying and blocking malicious IP addresses, we can also take precautions by limiting the access frequency of low-frequency crawlers. We can set a reasonable access frequency threshold based on the statistical access data and limit the access frequency in the back-end program of the website. When we find that the access frequency of an IP address exceeds the preset threshold, we can temporarily block the IP address to prevent it from causing excessive access pressure on the website.

Sample code:

from flask import Flask
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address

app = Flask(__name__)
limiter = Limiter(app, key_func=get_remote_address)

@app.route('/api')
@limiter.limit('10 per minute')
def api(): return 'Hello, World!
return 'Hello, World!

concluding remarks
In the field of network security, preventing low-frequency crawler attacks is a complex and important task. By analyzing the behavioral patterns of low-frequency crawlers, using IP proxy identification techniques and restricting access frequency, we can effectively protect the security and stability of websites. Of course, to achieve real network security, we also need to constantly learn and study the latest attack methods and prevention techniques to cope with the ever-changing network security threats. May we be able to work together to build a more secure network environment!