Bot Traffic Detection in Web Logs

AttackPatterns Web Application Bot Traffic / Scraping

What This Means

Detect and classify bot traffic in your web server logs. Distinguish between legitimate search engine crawlers, good bots, and malicious automated traffic consuming your server resources.

Example Log

-- Access log showing bot traffic patterns:
203.0.113.1 "GET /products/1 HTTP/1.1" 200 5120 "-" "Mozilla/5.0" (0.1s between requests)
203.0.113.1 "GET /products/2 HTTP/1.1" 200 5120 "-" "Mozilla/5.0" (0.1s between requests)
203.0.113.1 "GET /products/3 HTTP/1.1" 200 5120 "-" "Mozilla/5.0" (0.1s between requests)
-- 10 requests per second, sequential URL pattern, no CSS/JS/image requests

Indicators of Suspicious Activity

Requests at inhuman speeds (10+ pages per second from one IP)
Sequential URL patterns suggesting systematic crawling or scraping
No associated requests for CSS, JavaScript, images, or fonts (headless browser)
Identical session behavior: no scrolling, no mouse movement, instant form submissions
Traffic from data center IP ranges (AWS, Azure, GCP, DigitalOcean)
Requests ignoring robots.txt directives

How to Investigate

Analyze request timing patterns to distinguish human browsing from bot behavior
Check whether requests load associated resources (CSS, JS, images)
Verify claimed bot identity using reverse DNS for search engine crawlers
Measure the impact on server resources (bandwidth, CPU, database queries)
Categorize bots: search engines, monitoring, scraping, malicious scanning
Check if bot traffic is inflating analytics metrics

Recommended Mitigations

Implement robots.txt with appropriate directives for legitimate crawlers
Deploy a bot management solution (Cloudflare Bot Management, DataDome, etc.)
Use rate limiting to throttle excessive requests from single IPs or sessions
Implement CAPTCHA or JavaScript challenges for suspected bots
Block data center IP ranges from accessing content meant for end users
Monitor and alert on sudden traffic spikes from new or unusual sources

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Related Log Types

Related Attack Patterns

Frequently Asked Questions

What percentage of web traffic is bots? ▼

Industry reports consistently show that 40-50% of all internet traffic is automated bot traffic. Of that, roughly half is good bots (search engines, monitoring) and half is bad bots (scrapers, attackers, spammers).

How do I distinguish good bots from bad bots? ▼

Good bots typically: identify themselves in User-Agent, respect robots.txt, crawl at reasonable speeds, and come from verifiable IP ranges. Bad bots: spoof User-Agents, ignore robots.txt, crawl aggressively, and use rotating proxies.

What is the business impact of bad bot traffic? ▼

Bad bots cause: content scraping and intellectual property theft, price scraping by competitors, inventory hoarding, ad fraud, inflated analytics, increased server costs, and degraded performance for real users.