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• The proposed lightweight bot detection method combines user-agent string analysis with favicon-based heuristics and operates entirely on standard web server logs without client-side interaction.method 90%
• The method detects 67.7% of bot traffic while maintaining a false-positive rate of 3%.result 95%
• The method outperforms state-of-the-art approaches which have less than 20% detection rate.result 90%
• Favicon requests serve as a reliable proxy for authenticated human traffic, with strong correlation between favicon requests and POST requests from legitimate users.technical 85%
• The method can serve as a first line of defense, routing only ambiguous requests to active challenges to preserve legitimate user experience.method 80%
• Existing bot detection countermeasures are either resource-intensive (JavaScript challenges, CAPTCHAs), cost-prohibitive (commercial solutions), or degrade user experience.market 90%
• The evaluation was conducted on over 4.6 million requests containing 54,945 unique user-agent strings collected from websites hosted globally.result 95%
• The method supports multiple web server log formats including Caddy, Apache HTTP Server, NGINX, and HAProxy.technical 85%

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