Why IP addresses are banned

This article systematically analyzes the technical principles and triggering mechanisms of IP address bans, explores the response strategies in enterprise-level scenarios, and recommends abcproxy as a solution for high-availability IP resource pools.

1. 3 Core Reasons Why IP Addresses Are Blocked

High-frequency requests trigger anti-crawling mechanism

Technical logic: The target website identifies crawler behavior by counting the number of requests from a single IP per unit time. For example, Google Search Console triggers the verification mechanism when the number of search requests from the same IP exceeds 5 per second.

Typical scenarios:

E-commerce price monitoring: A price comparison tool initiates 10 product detail page requests per second

Social media data scraping: The crawler without setting a delay continuously visits Twitter/X user's homepage

Defense threshold: Mainstream platforms usually set a threshold of 20-50 requests per minute, and some strict platforms (such as Cloudflare) may be as low as 10 requests per minute.

Abnormal behavior pattern recognition

Behavioral signature library matching:

Analyze user clickstream data through machine learning and establish a normal human operation benchmark model. For example:

The page dwell time is lower than the industry average (the average dwell time on e-commerce pages is about 90 seconds)

Mouse movement trajectories present programmed features (such as precise straight line movement)

Device fingerprint detection:

Collect more than 200 parameters such as browser Canvas fingerprint and WebGL rendering features to generate a unique device ID. Requests initiated by abnormal devices (such as those without GPU acceleration capabilities) will be blocked.

IP reputation database matching

Blacklist database:

Commercial-grade anti-crawl services (such as Distil Networks) maintain real-time updated IP blacklists that cover known data center IP segments and proxy service IP pools.

Associated risk determination:

If other IPs in the same C segment have malicious behavior, the entire subnet may be blocked. For example, after a host is infected with the Mirai botnet, the IPs in the same /24 segment will be marked as high risk.

2. Enterprise-level IP blocking response strategy

Dynamic IP resource pool technology

Residential proxy Rotation:

Use residential IPs from operators such as British Telecom (BT) and Deutsche Telekom (DT), and switch to different end-user IPs for each request. On average, each address in abcproxy's IP pool is only used 1.2 times, significantly reducing the probability of being blocked.

Protocol layer masquerade:

Injecting unconventional TCP window sizes (such as 1027 bytes) into the TCP handshake mechanism and enabling technical features such as TCP Fast Open make the traffic closer to home broadband characteristics.

Request traffic shaping control

Adaptive rate limiting algorithm:

Dynamically adjust the request interval based on the target website response code. For example, when a 503 status code is detected, the request interval is automatically extended from 2 seconds to 30 seconds.

Human-computer behavior simulation:

Inject random mouse movement tracks into the Selenium script, set the page scrolling speed fluctuation range (0.5x-1.5x), and configure a reasonable resource loading strategy (such as delayed image loading).

Distributed architecture design

Edge computing node deployment:

Deploy request distribution services at edge nodes such as AWS London and Google Frankfurt to disperse single-source traffic to different geographic regions.

Blockchain Evidence Storage System:

Use Hyperledger Fabric to record the usage of each IP, ensure that the IP allocation process is auditable, and avoid internal resource abuse leading to bans.

3. IP Blocking Detection and Avoidance Technology Frontier

Deep Packet Inspection (DPI) Countermeasures

TLS fingerprint customization:

Modify the ClientHello extension order, elliptic curve priority and other parameters in the TLS handshake process to make the encrypted traffic characteristics fully match Chrome version 120+.

HTTP/2 frame injection:

Insert non-standard pseudo-headers (such as authority) into the request header to simulate the protocol implementation details of the latest version of the Edge browser.

Reinforcement Learning Model Training

Construct a virtual confrontation environment, and the proxy learns the optimal request strategy through the Q-Learning algorithm:

State space: Contains 15-dimensional features such as response code, response time, HTML tag change rate, etc.

Reward function: Successfully obtain the target data reward +1, trigger the verification code penalty -0.5, IP is banned and training is terminated

Experiments show that after 200,000 iterations, the model reduced the ban rate from 78% to 9% without using a proxy.

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