How to Use Machine Learning to Detect Bot IPs

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When it comes to detecting detect bot IPs, there are two common techniques: server-side log analysis and client-side JavaScript analysis. While these methods are effective, they may not meet your organization’s needs.

However, with the proliferation of bad actors on the web, some companies have turned to machine learning to detect bot traffic. By combining the above techniques with a web analytics tool, organizations can effectively monitor bot activity and protect their website.

A web application firewall can be used to block malicious traffic from hitting your website. It can also be used to customize rules to identify attacks.

To detect bot traffic, you can look at data on user behavior and traffic patterns. A sudden increase in pageviews or bounce rates is an indication that bots are attempting to access your site.

Another way to detect bot activity is through device fingerprinting. This can help identify suspicious scripts and virtual machines. Also, if your IP addresses are on blacklists, this could indicate bot activity.

The use of CAPTCHAs is a common technique to stop bots from accessing your site. However, a sophisticated bot can bypass these security measures.

Another way to detect bot traffic is to use rate limits. If a single IP address is hitting your site more than 100 times per day, this could be a sign of automated traffic. Rate limits will slow the attack so that you can intervene.

Other ways to detect bot traffic are through behavior monitors and velocity checks. These methods leverage machine learning to detect irregular event sequences.

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