How to Detect Bot Traffic?

How can bot traffic be identified?

To identify good bots and prevent bad bot traffic, websites have created various bot detection techniques. Here are several ways they do that:

• Browser fingerprinting – this refers to information that is gathered about a computing device for identification purposes (any browser will pass on specific data points to the connected website’s servers such as your operating system, language, plugins, fonts, hardware, etc.) Learn more about what is browser fingerprinting in our in-depth blog. 

• Browser consistency – checking the presence of specific features that should or should not be in a browser. This can be done by executing certain JavaScript requests.

• Behavioral inconsistencies – nonlinear mouse movements, rapid button and mouse clicks, repetitive patterns, average page time, average requests per page, and similar, bot-like behavior.

• CAPTCHA – a popular anti-bot measure are CAPTCHAs – a challenge-response type of test that often asks you to fill in correct codes or identify objects in pictures. You can read up more on how CAPTCHAS work in our blog.

Once a website identifies bot-like behavior, it blocks them from further crawling. For more details, Dmitry Babitsky, the co-founder & chief scientist at ForNova, has spoken in-depth on how websites block bots in his presentation at OxyCon. 

Bot detection challenges

Distinguishing bot traffic from human behavior online has become a complex task in itself, and the bots on the internet have evolved dramatically over the years. Currently, there are four different generations of bots:

• First-generation – these bots are built with basic scripting tools and mainly perform basic automated tasks like scraping, spam, etc.

• Second-generation – mainly operate through website development, hence ending up with the name ‘web crawlers.’ They are relatively easy to detect due to the presence of specific JavaScript firing and iframe tampering.

• Third-generation – often used for slow DDoS attacks, identity thefts, API abuse, and others. They are relatively difficult to detect based on device and browser characteristics and would require proper behavioral and interaction-based analysis to identify.

• Fourth-generation – the newest iteration of bots. Such bots can perform human-like interactions like nonlinear mouse movements. In order to detect such bots, advanced methods, often involving the use of AI and machine learning technologies are required.

The fourth generation of bots are tough to differentiate from legitimate human users, and basic bot detection technologies are no longer sufficient. For such bot traffic to be detected, it will take a lot more than simple tools and behavioral interaction analysis. 

Overcoming anti-bot measures

If you want a step by step guide on how to crawl a website without getting blocked by the anti-bot measures, we have written in great detail on how to do exactly that. In that blog post, we provide you with a list of actions to prevent getting blacklisted while scraping and crawling websites. However, if you would like a faster and less labor-intensive method, you could check out Web Unblocker as a solution.

It is an AI-powered proxy solution that has a dynamic fingerprinting feature. This feature allows Web Unblocker to choose the right combination of headers, cookies, browser attributes, and proxies so that you can appear as an organic user and successfully bypass all target website blocks. 

Conclusion

Bad bot traffic is predicted only to increase each year. As for good bot traffic, the chance to not get mixed in with the bad crowd is slowly dwindling. Amongst good bots there are a lot of web scrapers that use gathered data for research, pulling down illegal ads, market research, etc. All of them may get flagged as bad and blocked. Fortunately, solutions implementing AI and ML technologies are being built to overcome false bot blocks.