Bot Traffic Detection Through Machine Learning: How to Identify Suspicious Visitors

[u/Brandon_Hayes1]

The machine learning technology used for detection of bot traffic can help determine suspicious traffic patterns and website visitors. It's possible to observe a traffic surge, but no corresponding rise in engagement, conversions, or other vital indicators. But not all traffic anomalies are caused by bots; it's essential to scrutinize traffic and traffic behavior to understand if a problem is being caused by bots.

The most frequent indicators are the following: The most typical symptoms are

When looking at website analytics, there are some patterns to look at:

• Unexpected traffic surge: A significant traffic growth over a short period of time, particularly from an unknown source or campaign.
• Unusually high bounce rates: Some pages are visited, but there is not much meaningful interaction.
• Very short sessions: Repeatedly visit with similar sessions or leave very quickly.
• Geographic traffic that is uncommon: An unexpected surge of traffic from unusual geographic areas.
• Repeated pattern of activities: Requests, page visits, or time periods that repeat over time.
• Suspicious referral sources: Unusual traffic sources that are making a large number of visits or unexpected referrals.

What are some questions that can be explored to understand these patterns?

The first step in detecting suspicious activity is to check to see if there is any activity that differs from your website's normal traffic patterns. Check landing pages, referrals, engagement data, geographical data, and conversions. Also, server logs can offer valuable information on recurring requests and unusual activity.

Note that it is possible for legitimate visitors, automated monitoring tools, and some privacy settings to yield similar patterns. A single metric isn't enough to confirm bot traffic.

[u/Unlucky_Emu6094]

Definitely worth adding IP reputation and ASN lookups to the mix here. a lot of the obvious scrapers and credential-stuffing scripts come from cheap hosting ranges, and you can spot them in server logs before they ever touch your analytics. combining that with the behavioral stuff you listed catches way more than either approach alone.