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An Overview Of Website Bot Detection

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Currently, it is estimated that nearly half of the internet traffic comprises website bots. Amidst this, there are legitimate bots that perform essential functions like content aggregation, indexing web pages, crawling and searching, and much more. Conversely, there are malicious bots that have been on a rapid increase in the recent past that are a cause for concern. 

To protect your website, you need to apply the proper website bot detection and protection mechanism. A legitimate bot sends a user-agent that indicates that it is a bot. Malicious bots tend to disguise themselves as legitimate visitors to your website. Because they do not send any recognizable User-Agent, it becomes impossible to detect them reliably.

Need for Website Bot Detection

Identification and detection of website bots are essential for your website. It helps classify the bots according to their legitimacy. You can therefore distinguish between a good bot and a malicious one. Below are reasons why website bot detection is essential to your website or online platform.

Proper evaluation of performance and analytics

Web traffic is an essential metric in evaluating the performance of a website. With numerous traffic currently being generated by web bots, developing a suitable website bot detection mechanism can help identify legitimate website traffic from bot traffic. Such skewed traffic inflates campaign data, product metrics, and analytics. 

By polluting this metric, bots disrupt funnel analysis and inhibit KPI tracking. Accurately separating the web traffic can help you develop efficient web bot detection methods and have reliable e-customer behavioral patterns. Therefore, you can rely on analytics to make proper decisions.

Protecting you from account takeover

Malicious bots can take over your account through brute force and credential stuffing attacks on your website. In credential stuffing, a website bot performs automated injections of user credentials stolen to various web applications. It is a mechanism that cybercriminals use to validate the stolen user credentials, and web bots hasten the process. 

When validated, they can log and perform authorized transactions as genuine users without the victim’s knowledge. High-value targets for account takeover attacks are e-commerce, financial, and healthcare institutions. 

Software-as-a-Service (SaaS) applications are, for the same reasons, susceptible to such automated attacks. Since their number of failed login attempts is high, distinguishing between malicious and legitimate website bots or users could be the solution. 

Account takeover and credential stuffing can lead to fraudulent transactions and unauthorized access to your account and all other user accounts on your website’s server.

Protection against API abuse

There has been a mounting increase in data breaches and attacks on poorly protected APIs. Fraudsters use intelligent web bots to exploit the vulnerabilities in the API and steal sensitive data like critical content for the business and user information. Since APIs are added and used routinely by the business, API security is not a one-time exercise. 

Website bot detection ensures that the usability of the API is not affected by removing and preventing malicious bots from your website. Investing in real-time malicious bot protection software can help prevent API abuse.

– Prevention of carding attacks

By deploying website bots on banking and e-commerce websites, fraudsters can test for the validity of stolen credit card numbers or develop an exclusive cardholder’s dataset. The web application firewalls cannot detect cracking and carding attempts. 

Loyalty Programs and gift cards are the most susceptible to this form of cybercrime.

Below are signs of a carding attack

  • Reduction in average basket value on your e-commerce account
  • A high number of failed payment authorizations
  • Cart abandonment rates are high
  • Increase in the number of transaction disputes and chargebacks

Carding and online fraud have many impacts on a business. When you accept a stolen credit card, products may be lost, and change back penalties increase which are a loss to any business setting. Due to the high number of change backs, merchant accounts can be terminated. In addition, the stolen reward points tarnish the reputation of a brand. Web bot detection can help minimize or eliminate this risk. 

Additionally, a website bot detection method helps you avoid; content and price scraping, digital Ad fraud, and layer 7 DDoS attacks.

Website Bot Detection Techniques

Many techniques can be applied to detect and combat website bots. At the core of these techniques is developing statistical and heuristic models that can identify behaviors that are non-human-like. These behaviors may include; consistency in the regularity of incoming requests and a similar total number of requests from an IP per specific time frame.

The website bot detection methods can be divided into three categories; 

  • Network-level bot detection 
  • Behavior-level detection
  • Device-level detection

1- Network-level Bot Detection

This website bot detection method is based on network characteristics after analyzing the flow of network traffic at constant intervals. High network activity can be a pointer of bot activity. Sophisticated bots use a slow-and-low approach to circumvent the aggregate detection. 

They do this by distributing the attack over multiple IPs for an extended period.

  • HTTP fingerprinting

By analyzing the traffic sent by a user to the server through HTTP fingerprinting, meaningful information about the site’s visitor can be revealed. It includes; the user-agent (the kind of browser used -edge, chrome, and the version), request headers like cookies and the encoding accepted by the user agent, the order of the request headers, and the IP address of the visitor.

  • The TCP/IP stack fingerprinting

The TCP stack contains details like; the packet size, initial TTL, the window size, the maximum size of the segment, the window scaling value, flags like nop, sackOk, and don’t fragment. When these variables are combined, they form a digital signature that can detect a website bot. 

Using open-source tools like p0f can help determine if a user is being forged, behind a NAT network, or has a direct internet connection.

  • TLS fingerprinting

During the SSL handshake between the web browser and the web server, a TLS fingerprint is generated. During an SSL handshake, the SSL version, accepted ciphers, extensions, elliptic curves, and curve formats are gathered by the JA3 fingerprinting library. The JA3 library then combines their decimal values using a “,” for delimiting each field and a “- “for delimiting each value. The string is then hashed into a 32-character MD5 fingerprint for easy sharing. 

This hash can be used to identify the legitimacy of a visitor.

2- Behavior-level Detection

This method of web bot detection uses behavioral and statistical variations. If the number of requests emanating from an IP per specific time frame increases exponentially, it may seem malicious or even suspicious. 

The JS rendering engine detects this behavior change. The bot detection algorithm is updated and deployed instantly to all data centers to provide real-time protection.

The other web-bot detection mechanism is device-level detection. It prevents bots that mimic the attributes of a device in an attempt to bypass security measures. 

Using AI-driven fuzzy matching, device ID, and device recognition, you can determine if it is a valid visitor or a bot.


With automated bots increasing daily, there is a corresponding increase in the threats paused. Bot management is essential for a business that values its digital assets, information, and user privacy. There are both legitimate and malicious bots out there, and investing in a cost-effective and efficient solution like DataDome is crucial in web bot detection. 

DataDome protects your website from malicious web bots that prevent the above cyber issues and good traffic whose analysis can help a business in decision-making.


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