How Do E-Commerce Sites Detect Bots to Prevent Fake Orders?

The term “fake orders” is no stranger to most people, especially in the e-commerce industry. Fake ordering has long been a major challenge for merchants. Whether it’s to boost sales, increase positive reviews, or earn promotional rewards, some dishonest users turn to automation tools or scripts to mimic real user activity and generate large volumes of fraudulent orders.

This not only distorts merchants’ data statistics but also disrupts market order and creates a poor user experience for genuine customers. As a result, anti-fake-order websites are placing increasing emphasis on bot detection.

So, how exactly can anti-fake-order websites implement effective bot detection? This article will provide a detailed analysis.

What Is Bot Detection?

As the name suggests, bot detection is the process of using technology to determine whether website visits or ordering actions are performed by real users or by automation tools and scripts. Modern bots are becoming increasingly sophisticated, capable of simulating mouse clicks, scrolling, keystrokes, and more. Without proper detection mechanisms, it can be very difficult to distinguish bots from humans.

Common detection methods include:

1. Browser Fingerprinting and Plugin Data

Browser fingerprints consist of information such as operating system, browser type, fonts, Canvas rendering, WebRTC status, and more. By analyzing these attributes, systems can detect whether the browsing environment is being controlled by automation tools.

2. User Behavior Analysis

This involves analyzing user interactions on the page, such as clicks, scrolling, and typing. Genuine user actions typically contain randomness or slight delays, while bots often complete consecutive actions at extremely high speeds.

3. IP and Network Characteristic Analysis

By monitoring IP addresses, geolocation, access frequency, and request patterns, websites can identify abnormal behavior.

How Do Anti-Fake-Order Websites Handle Bot Detection?

For e-commerce platforms and anti-fake-order websites, bot detection is not only about spotting abnormal access—it’s also about taking targeted measures aligned with business scenarios, such as:

1. Multi-Layered Verification Mechanisms

Beyond front-end behavior analysis, backend verification can also be added, for example:

• Triggering CAPTCHA challenges for suspicious actions

• Restricting multiple orders from the same IP address or account

With multi-layered protection, platforms can effectively reduce fake order risks without disrupting the genuine user experience.

2. Using Professional Bot Detection Tools

There are many bot detection solutions on the market, such as ToDetect, which analyzes attributes like browser fingerprinting, JavaScript execution, WebRTC state, Canvas rendering, and more to determine whether the browsing environment is under automated control.

Conclusion

For anti-fake-order websites, bot detection is not just a technical challenge—it’s also a crucial measure to ensure platform fairness and improve user experience. By combining methods such as behavior analysis, browser fingerprinting, and IP/network analysis, platforms can effectively detect fraudulent activities and take corrective measures.

If you’re looking for a quick way to implement anti-fake-order protections, you can also use the ToDetect Bot Detection feature. This detection system can identify whether automated activities exist in a browser or script. By analyzing browser fingerprints, JavaScript execution, WebRTC state, Canvas rendering, navigator object, plugin data, and more, it can determine whether the browsing environment is controlled by automation tools or real users.

Currently, mainstream bot detection and human verification systems like Cloudflare Turnstile, Google reCAPTCHA, and hCaptcha also integrate similar mechanisms, performing implicit assessments without requiring additional user actions.