A sudden rise in bot traffic is a signal to investigate, not proof of an attack. First establish whether users or your infrastructure are affected, then compare edge and origin logs with normal traffic, classify the clients, and apply the narrowest effective control. A spike can come from useful crawlers, scraping, an application event, or a rate-limit or firewall rule affecting requests.
1. Confirm the impact before changing controls
Check whether visitors are experiencing slow pages, errors, failed logins, or checkout problems, and whether origin load has increased. Record when the change began, the affected hostnames and paths, and what users or internal monitoring report. This helps distinguish a noisy traffic chart from an incident that is degrading service.
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Do not infer a DDoS attack or malicious scraping from volume alone. Look for correlated evidence in request patterns, service health, and security events. If service is impaired or the traffic appears volumetric, involve your hosting, CDN, or WAF provider and follow your incident process; the cited provider guidance does not establish a universal escalation threshold.
2. Compare the spike with a normal period
Review edge and origin logs for the same time window and compare them with a representative period of ordinary operation. Microsoft recommends checking WAF and access logs for a sudden change in request rate, client IP count, geography mix, user-agent distribution, and requested URIs (Microsoft Learn: Application (Layer 7) DDoS protection).
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- Request rate and timing: establish when the increase began and whether it is sustained, bursty, or tied to a particular event.
- IPs and geography: note whether requests are spread across clients or concentrated. Neither an IP address nor a location alone proves intent.
- User agents: look for changes in the mix, but do not trust a claimed crawler identity without verification.
- Paths and queries: identify whether traffic targets ordinary pages, expensive searches, login or checkout flows, APIs, or unusual query patterns.
- Status codes and control events: check especially for 429 responses and for WAF/CDN rate-limit, challenge, or block decisions.
These are clues for classification, not standalone proof that a client is malicious. Check both edge and origin views where possible: a control may stop a request before it reaches the origin, while origin logs alone may not explain what happened at the edge.
3. Decide what kind of automation you are seeing
Separate verified, useful crawlers from suspicious or costly automation before blocking. A search or monitoring crawler may be legitimate; a scraper or evasive client may impose load or target sensitive routes. Inspect what the client actually requests, how it behaves, and whether it creates measurable service impact.
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Use the verification and bot-classification features available in your own provider rather than relying solely on a user-agent string. AWS distinguishes common protection, which identifies self-declared bots, from targeted protection that also detects bots concealing their identity (AWS WAF Bot Control). Classification features and labels vary by product and configuration, so review the events and requests behind a label before acting on it.
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4. Check whether your own rules are contributing
A rise in 429 responses can mean a rate limit is working, but it can also reveal that a threshold or matching condition is catching legitimate use. Review which rule generated the response or decision, which path or client it matched, and whether the event aligns with the start of the symptom. Also inspect firewall and CDN decisions for unexpected challenges or blocks.
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Identify the responsible control before loosening or tightening anything. Challenge pages can disrupt real users and integrations, particularly APIs and critical flows; a broad change made without tracing the rule can worsen the incident or hide its cause.
5. Choose the narrowest mitigation that fits the pattern
Use the evidence to select a control and scope it to the behavior, URI, session, or bot category involved. Provider examples illustrate possible approaches rather than universal settings.
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| Observed pattern | Possible response | What to watch |
|---|---|---|
| High request volume to a costly or sensitive URI | Apply a rate-based rule scoped to that URI or relevant session, where the provider supports it. | Unwanted request reduction, legitimate usage, and 429 events. |
| Automated requests repeatedly producing origin 403 or 404 responses | Investigate the source and consider a scoped rate limit. Cloudflare describes rate limiting high-volume traffic that triggers many origin 403 or 404 responses as a general identification approach (Cloudflare: Rate limiting best practices). | Whether the requests are genuinely unwanted and whether real users or crawlers share the same pattern. |
| Bot classification suggests automation, but intent or legitimacy is uncertain | Use an observation or challenge action if available, then review labels and events before blocking. | False positives, challenge completion, and effects on APIs or other integrations. |
| Verified harmful traffic with a clear, narrow match | Block the matching category, behavior, or route using the available WAF/CDN control. | Continued legitimate access and whether the traffic shifts to other routes or patterns. |
Cloudflare documents combining bot scores with rate limits and session cookies for more targeted decisions (Cloudflare: Bot Management). AWS describes rate-based controls for high-volume activity and sensitive URIs (AWS WAF rate-based rule statement). These examples depend on the provider’s products, plan, and configuration; neither implies that a particular service is required.
Do not copy a sample request threshold as a safe universal limit. A tolerable rate depends on the site’s normal baseline and the impact of the URI. Some targeted detection features also need observations during normal operation to establish a baseline, so enabling one during an incident may not immediately provide mature detection (AWS WAF Bot Control).
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6. Validate, adjust, and document the result
After applying a control, watch both the unwanted traffic and the visitor experience. Confirm that the targeted requests decline, legitimate users can still complete important flows, and challenge, block, and 429 events match your intent. AWS advises reviewing bot labels and ensuring legitimate traffic is not mislabeled before moving to block mode (AWS WAF Bot Control).
If a rule creates false positives, narrow its scope or revert it, then check whether the underlying symptoms remain. Once the incident has passed, remove temporary rules that are no longer needed; retain controls only when their matching behavior and side effects are understood. Record the timeline, traffic pattern, decisions, observed false positives, and any thresholds you learned for future incidents.
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