We do not store traffic originating from bots, invalid clicks, or duplicate clicks. Only one job per IP address is stored in our system. If a bot lacks a JavaScript engine and exhibits request or resembles cURL-like behavior, it is completely ignored. However, if a bot impersonates a user and performs automated activities, such as using Selenium or Puppeteer with unclear headers, the traffic is temporarily retained and analyzed for possible bot-like activity. If the traffic is identified as coming from a bot, it is removed from our statistics, and the client is blocked through the firewall.
Most bots do not have a JavaScript engine. If they do, they often visit links designed as 'honeypots,' and they usually move rapidly or follow a monotonous pattern. This allows us to differentiate between legitimate traffic and bots.
Please see the full list of methods:
Distinguishing between bots and genuine website visitors can be a challenging task, as bots are becoming more sophisticated and attempt to mimic human users. However, several methods and techniques can be employed to make this distinction:
1. User-Agent Analysis: Browsers and bots often have different User-Agent headers. Analyzing the User-Agent of the visitor allows a website owner to determine if the request is from a legitimate browser or a bot.
2. IP Address Check: Bots can often be identified based on their IP address. Some bots use known IP addresses, or they belong to IP ranges associated with well-known botnets.
3. JavaScript Check: Bots frequently lack a JavaScript engine and cannot perform certain JavaScript interactions. Checking JavaScript functionalities can help determine if the visitor is a bot or a genuine user.
4. Captchas and reCAPTCHAs: Implementing captchas or Google reCAPTCHA can assist in blocking bots, as they struggle to handle the human validation requirements.
5. Time Interval Analysis: Bots can make rapid and consistent requests, while genuine users are typically more unpredictable. Analyzing the time intervals between requests can distinguish bots from legitimate visitors.
6. Behavioral Analysis: Analyzing visitor behavior on the website, such as mouse click patterns and page browsing behavior, can identify suspicious activities that may indicate a bot.
7. Blacklists and Whitelists: Maintaining blacklists of known bots and whitelists of recognized legitimate search engines and services can help filter traffic.
8. Geolocation: Identifying visitors based on their geolocation can aid in blocking bots from specific regions.
It is essential to apply a combination of these methods and techniques to achieve a reliable assessment and ensure that legitimate visitors are not affected by security measures. No single method is foolproof, so it is crucial to continually update detection and security mechanisms to keep up with ever-changing bot technologies.
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